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Editorial: Vote for Terry Wiley, best choice for new Alameda County DA
by Pleasanton Weekly editorial board
Uploaded: Wed, Oct 26, 2022, 4:47 am 1
Civil rights attorney Pamela Price and veteran prosecutor Terry Wiley are vying to succeed retiring Alameda County District Attorney Nancy O'Malley. The race is on the November ballot because none of the candidates received the majority of votes to win outright in the June primary.
Wiley is the candidate with the requisite experience and knowledge to fill the role of the county's top prosecutor and lead a $100 million government agency with approximately 425 lawyers, investigators and administrative staff members.
Wiley has positioned himself as a "public safety" candidate, and has the record to back that up.
With more than 32 years in the county's DA office, Wiley was promoted to Assistant II District Attorney in 2020. He has vast criminal law experience, has had many different assignments over the years and has prosecuted everything from misdemeanors to murders. He has shown he will stand up for justice regardless of the person committing the crime, including prosecuting rouge police officers like the Oakland "Riders" in the early 2000s.
He has earned the endorsement of O'Malley, who tweeted she is "firmly behind the election of Terry Wiley" to succeed her.
A strong advocate for criminal justice reform and fighting inequalities in the adult and juvenile justice systems, Wiley is the director of the DA office's new Division of Diversity, Equity and Inclusion, which is focused on prosecuting crimes while making sure everything is carried out fairly and with integrity.
Wiley's managerial background includes his current role as the director of human resources at the DA's office and his previous position leading the felony trial team, which boasted a 93% conviction rate.
Wiley's opponent, Price, has never prosecuted a case.
Price has run her own civil rights litigation practice for roughly 30 years and advocates criminal justice reform by creating a "collaborative" criminal justice system that offers solutions to reducing crime other than incarceration, such as jail diversion and restorative justice programs for young offenders.
While Price says she wants to decrease incarceration of young people, a laudable goal, she has no record of executing programs that produce results. On the other hand, Wiley led a nationally recognized juvenile division, where he addressed ethnic disparities of juveniles in custody and worked with the probation department to cut incarceration rates by two thirds.
We agree with Price that the grievous concerns at the county's Santa Rita Jail, such as in-custody deaths and alleged abuse by guards, must be addressed immediately.
She has tried to blame Wiley for the issues at the jail, but the accusation doesn't hold up to scrutiny; implying he is guilty simply by being part of the DA's office is absurd. In fact, Wiley is the best person to address the problems at the sheriff-operated jail because he has a track record of taking action as opposed to lamenting problems and pointing fingers.
Price has also tried to distance herself from "progressive" DAs like San Francisco's recently recalled Chesa Boudin. She has said "I am not Chesa Boudin," but many of her ideas mirror Boudin's, like prohibiting the use of the "three strikes" law and "gang enhancements" to increase sentences.
There is no doubt that change will be a welcome sight, but it will be more effective and timely when implemented by someone who knows the office and the job.
Alameda County needs and deserves a DA with prosecutorial experience who has also initiated programs addressing ethnic disparities and has actually led programs that have reduced incarceration rates without affecting public safety. That candidate is Wiley.
Vote for Terry Wiley for DA.
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by Pleasanton Weekly editorial board /
Uploaded: Wed, Oct 26, 2022, 4:47 am
Bay Area Native
Bay Area Native, Pleasanton Valley
"I am not Chesa Boudin." Price is endorsed by numerous "activists" who are contemptuous of law enforcement and view criminals as victims. While she may claim she is not like Chesa Boudin she is proudly endorsed by Phildelphia DA Larry Krasner.
Kranser's accomplishments in Philly: 1,000 people murdered this year (as of Tuesday) compared to 535 for the same period 2016-17. Robberies using a gun +160%, aggravated assaults with a gun +62% versus 2017. Krasner took office in 2017. Krasner pledged to no longer seek cash bail for 25 offenses including retail theft and DUI - a similar approach advocated by Price (and Boudin).
Let's save ourselves one or two years of lawlessness and the cost of recalling Price by electing Wiley. Click the link to see Price's endorsements (note that Julie Testa is listed).


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\subsubsection{Implementation Details}
We implement our experiments using PyTorch\footnote{\hyperlink{https://pytorch.org}{https://pytorch.org}} deep learning framework. For StyleGAN2 \cite{karras2020analyzing} architecture in the case of similar domains, we use the popular PyTorch implementation \footnote{\hyperlink{https://github.com/rosinality/stylegan2- pytorch}{https://github.com/rosinality/stylegan2- pytorch}}. We attach all source code that reproduces our experiments for the setting with similar domains as a part of the supplementary material named "adaptation\_to\_similar\_domains". We also provide configuration files to run each experiment.

\subsubsection{Hyperparameters}

For the text-based domain adaptation we use the optimization loss from \Cref{eq:direction_loss}. As pretrained CLIP Vision-Transformer \cite{radford2021learning} we utilize "ViT-B/32", "ViT-B/16" models.
We use batch size of 4 and different number of iterations depending on the domain (from 250 to 300 iterations). As optimizer we apply the ADAM Optimizer with parameters we provide in \Cref{tab:hyper_opt_td_im2im}.

For the one-shot image-based domain adaptation we apply the optimization loss from \Cref{eq:mtg_loss}. We set loss coefficients as $\lambda_{cw} = 0.5, \lambda_{rc} = 30, \lambda_{rr} = 10$. We use batch of 4 and 300 number of iterations. We utilize the ADAM Optimizer with parameters we provide in Table~\ref{tab:hyper_opt_td_im2im}.

\begin{table*}[!h]
    \centering
    \caption{ADAM optimizer hyperparameters for each setting.}
    \begin{tabular}{llll}
    \toprule
         Parameter Space & lr & betas & weight\_decay \\
         \midrule\midrule
         SyntConv & 0.002 & (0.0, 0.999) & 0 \\
         Full & 0.002 & (0.0, 0.999) & 0 \\
         Affine & 0.01 & (0.0, 0.999) & 0 \\
         Mapping & 0.3 & (0.0, 0.999) & 0 \\
         StyleSpace & 0.05 & (0.9, 0.999) & 0 \\
    \bottomrule
    \end{tabular}
    \label{tab:hyper_opt_td_im2im}
\end{table*}

\subsubsection{Quantitative Results}

For quantitative comparisons, we utilize Quality and Diversity metrics from \cite{alanov2022hyperdomainnet}. The Quality is measured as mean cosine similarity between CLIP embedding of the text description of the target domain and embeddings of generated images from the target domain. In the case of one-shot image-based domain adaptation, the embedding of the target domain is calculated as the CLIP embedding of the reference style image.
Diversity metric is estimated as mean pairwise cosine distance between CLIP embeddings of generated images from the target domain. As the CLIP image encoder we use only  ViT-L/14 that is not applied during training (in the training we use ViT-B/16, ViT-B/32 image encoders).

For every metric we set the number of synthesized images to \textbf{1000}. We evaluate each metric five times with different generated images to estimate error bars. However, we obtain that the error bar is less than $10^{-5}$ for all settings. So, we omit them in Tables.

We provide calculated metrics in \Cref{table:sim_domains_sg_tuned_parts} for a wide range of domains (text-based and image-based). Please see \Cref{fig:name_to_style_image_mapping} for the correspondence between the name of style image and its appearance. From the results in \Cref{table:sim_domains_sg_tuned_parts} we see that all four parameterizations Full, SyntConv, Affine and Mapping achieve the comparable Quality but the Mapping has significantly lower Diversity. It indicates that the Mapping parameterization collapsed to one image that maximizes the CLIP score (it can be also seen from qualitative results, see \Cref{appx:sim_qual_res}). At the same time, we observe that Affine parameterization demonstrates comparable performance to Full and SyntConv in terms of both Quality and Diversity. It confirms our results from the main part in \Cref{sec:importance} that Affine is sufficient for similar domains.

\begin{figure}[!h]
    \centering
  \includegraphics[width=\textwidth]{imgs/Mapping_name_style_image.pdf}
  \caption{Correspondence between names and style images for one-shot image-based domain adaptation.}
  \label{fig:name_to_style_image_mapping}
  \vspace{-0.5cm}
\end{figure}

\begin{table*}[!h]
\centering
\caption{Quality and Diversity metrics \cite{alanov2022hyperdomainnet} for text-based and one-shot image-based domain adaptation. Affine parameterization achieves comparable results as Full and SyntConv parameterizations.}
	\label{table:sim_domains_sg_tuned_parts}

\begin{tabular}{lcccccccc}
\toprule
 & \multicolumn{4}{c}{\textbf{Quality}} & \multicolumn{4}{c}{\textbf{Diversity}} \\
 \cmidrule(lr){2-5} \cmidrule(lr){6-9}
 & Full & SyntConv & Affine & Mapping & Full & SyntConv & Affine & Mapping \\
\midrule\midrule
\textbf{Text Domains} \\
\midrule
Anime Painting & $0.25$ & $0.25$ & $0.26$ & $0.22$ & $0.25$ & $0.25$ & $0.23$ & $0.13$ \\
Pixar Render & $0.27$ & $0.26$ & $0.27$ & $0.26$ & $0.21$ & $0.21$ & $0.20$ & $0.09$ \\
Sketch & $0.21$ & $0.19$ & $0.19$ & $0.18$ & $0.30$ & $0.31$ & $0.29$ & $0.14$ \\
Ukiyo-e Painting & $0.22$ & $0.22$ & $0.22$ & $0.23$ & $0.22$ & $0.22$ & $0.23$ & $0.07$ \\
Fernando Botero Painting & $0.31$ & $0.29$ & $0.29$ & $0.23$ & $0.23$ & $0.24$ & $0.24$ & $0.11$ \\
Pop Art & $0.25$ & $0.24$ & $0.24$ & $0.26$ & $0.27$ & $0.27$ & $0.33$ & $0.07$ \\
Werewolf & $0.26$ & $0.25$ & $0.22$ & $0.27$ & $0.13$ & $0.14$ & $0.17$ & $0.07$ \\
Zombie & $0.24$ & $0.23$ & $0.24$ & $0.24$ & $0.12$ & $0.12$ & $0.13$ & $0.07$ \\
The Joker & $0.25$ & $0.24$ & $0.24$ & $0.25$ & $0.17$ & $0.17$ & $0.17$ & $0.13$ \\
Disney Princess & $0.23$ & $0.22$ & $0.23$ & $0.20$ & $0.24$ & $0.28$ & $0.28$ & $0.11$ \\
Cubism & $0.23$ & $0.21$ & $0.19$ & $0.19$ & $0.22$ & $0.22$ & $0.25$ & $0.07$ \\
Tolkien Elf & $0.27$ & $0.26$ & $0.28$ & $0.28$ & $0.22$ & $0.23$ & $0.22$ & $0.10$ \\
Impressionism & $0.27$ & $0.25$ & $0.25$ & $0.24$ & $0.25$ & $0.24$ & $0.25$ & $0.09$ \\
Claude Monet & $0.21$ & $0.19$ & $0.19$ & $0.22$ & $0.24$ & $0.25$ & $0.27$ & $0.07$ \\
Modigliani & $0.27$ & $0.25$ & $0.23$ & $0.26$ & $0.20$ & $0.21$ & $0.24$ & $0.12$ \\
The Thanos & $0.23$ & $0.22$ & $0.23$ & $0.28$ & $0.29$ & $0.31$ & $0.31$ & $0.12$ \\
Edvard Munch Painting & $0.25$ & $0.23$ & $0.24$ & $0.22$ & $0.20$ & $0.22$ & $0.24$ & $0.09$ \\
Dali Painting & $0.26$ & $0.24$ & $0.25$ & $0.25$ & $0.22$ & $0.23$ & $0.22$ & $0.14$ \\
\midrule
\textbf{Image Domains} \\
\midrule
Anastasia & $0.64$ & $0.67$ & $0.64$ & $0.52$ & $0.28$ & $0.26$ & $0.27$ & $0.20$ \\
Mermaid & $0.64$ & $0.66$ & $0.63$ & $0.57$ & $0.26$ & $0.25$ & $0.27$ & $0.17$ \\
Titan Erwin & $0.65$ & $0.64$ & $0.64$ & $0.41$ & $0.28$ & $0.28$ & $0.28$ & $0.14$ \\
Titan Armin & $0.58$ & $0.57$ & $0.58$ & $0.53$ & $0.29$ & $0.29$ & $0.30$ & $0.10$ \\
Speed Paint & $0.65$ & $0.65$ & $0.63$ & $0.58$ & $0.31$ & $0.32$ & $0.33$ & $0.12$ \\
    \midrule\bottomrule
    \end{tabular}
  \vspace{-0.5cm}
\end{table*}

\subsubsection{Qualitative Results}
\label{appx:sim_qual_res}

We provide more samples for qualitative comparison of different parameterizations. \Cref{fig:fig3_like_0,fig:fig3_like_1} are extensions of \Cref{fig:sim_dom_parts} from the \Cref{sec:importance} that show more text-based and image-based domains. Also, we illustrate the diversity of generated samples for each parameterization in \Cref{fig:app_affine_sim_examples,fig:app_full_sim_examples,fig:app_syntconv_sim_examples,fig:app_mapping_sim_examples}. We observe that Full, SyntConv and Affine parameterizations demonstrate comparable visual quality and diversity. While the Mapping parameterization obtains poor adaptation quality and collapsed to one image almost for each domain (see \Cref{fig:app_mapping_sim_examples}).
We see that these figures agree with the quantitative results and it confirms the conclusions from \Cref{sec:importance}.

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/app_fig3_0.pdf}
  \caption{Text-based and one-shot image-based domain adaptation for different parameterizations. Affine achieves visual quality on par with Full and SyntConv parameterizations.}
  \label{fig:fig3_like_0}
  \vspace{-0.5cm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/app_fig3_ext_0.pdf}
  \caption{Text-based and one-shot image-based domain adaptation for different parameterizations. Affine achieves visual quality on par with Full and SyntConv parameterizations.}
  \label{fig:fig3_like_1}
  \vspace{-0.5cm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/appendix_sim_affine.pdf}
  \caption{Text-based and one-shot image-based domain adaptation for Affine parameterization. Uncurated random samples for each domain.}
  \label{fig:app_affine_sim_examples}
  \vspace{-0.5cm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/appendix_sim_full.pdf}
  \caption{Text-based and one-shot image-based domain adaptation for Full parameterization. Uncurated random samples for each domain.}
  \label{fig:app_full_sim_examples}
  \vspace{-0.5cm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/appendix_sim_conv.pdf}
  \caption{Text-based and one-shot image-based domain adaptation for SyntConv parameterization. Uncurated random samples for each domain.}
  \label{fig:app_syntconv_sim_examples}
  \vspace{-0.5cm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/appendix_sim_mapping.pdf}
  \caption{Text-based and one-shot image-based domain adaptation for Mapping parameterization. Uncurated random samples for each domain.}
  \label{fig:app_mapping_sim_examples}
  \vspace{-0.5cm}
\end{figure*}

\subsubsection{Implementation Details}

We use a modified version of the StyleGAN2-ADA Pytorch implementation\footnote{\hyperlink{https://github.com/NVlabs/stylegan2-ada-pytorch}{https://github.com/NVlabs/stylegan2-ada-pytorch}} for training all experiments with moderately similar and dissimilar domains. We downscale images from Metfaces, Mega, Ukiyo-e and Flowers datasets to match $512\times512$ resolution and upscale LSUN Car and Church datasets to the same resolution. Also, we use only 10K random subsets from LSUN Car and LSUN Church datasets and 1K random subset from Flowers dataset. We attach all source code that reproduces our experiments
for the setting with moderately similar and dissimilar domains as a part of the supplementary material named "adaptation\_to moderately\_similar\_and\_dissimilar\_domains".

\subsubsection{Hyperparameters}

We fine-tune all models to a child domain from the FFHQ512 checkpoint. Specifically, we use model config-f and the default hyper-parameters for \texttt{stylegan2} config, except learning rate, from the official Nvidia StyleGAN2-ADA Pytorch implementation. For the Affine+ (and for all models in Table~\ref{table:a.3.Affine+}), StyleSpace and StyleSpace+ parameterizations, we increase the generator learning rate from $0.002$ to $0.02$. For other parameterizations, we leave the learning rate unchanged. Also, we use \texttt{bgc} augmentation for all domains and parameterizations. We train models on a single Nvidia V100 GPU for 241K images, but for Mega and Ukiyo-e datasets we use intermediate checkpoints with 40K and 100K images respectfully.

\subsubsection{Quantitative Results}

For quantitative comparison, we compute FID5k (denoted as FID) using $5000$ generated images and all training images. We also compute FID50k and KID50k (denoted as KID) using $50000$ generated images and all training images.
These metrics in Tables~[\ref{table:a.3.FID50k},~\ref{table:a.3.KID50k}] reaffirm our observation that the Affine+ parameterization can close the gap with the Full parameterization for most of the domains. For the Car domain, the quality gap remains, although ten times less than for the Affine parameterization.

\input{tables/table_A.3.FID50k}
\input{tables/table_A.3.KID50k}

Exact Affine+ parameterization was selected by analyzing three different domains: Metfaces, Dog and Church. Table \ref{table:a.3.Affine+} demonstrates that Affine+64 has the best performance on these datasets and therefore is selected for the further analysis.

\input{tables/table_A.3.Affine+}

\subsubsection{Qualitative Results}

In this section, we provide generated images for all datasets (Metfaces, Mega, Ukiyo-e, Dog, Cat, Car, Church and Flowers) alongside with uncurated results for parameterizations and datasets.\\*\indent Figures~[\ref{fig:fig_A.3.1},~\ref{fig:fig_A.3_4.Samples}] show that Affine+ parameterization has comparable visual quality and diversity with Full and SyntConv parameterizations for all domains (including dissimilar ones) while having less trainable parameters by the factor of five. At the same time, Affine or Mapping parameterizations fail even on moderately similar domains (Cat, Dog).

\begin{figure}[!h]
  \centering
  \includegraphics[width=0.95\textwidth]{imgs/Figure_A.3.1.pdf}
  \caption{Domain adaptation for moderately similar and dissimilar domains. Affine+ parameterization produce results on par with Full one even for dissimilar domains (Cat, Church).}
  \label{fig:fig_A.3.1}
\end{figure}

\begin{figure}[!h]
  \centering
  \includegraphics[width=0.95\textwidth]{imgs/Figure_A.3_4.Samples.pdf}
  \caption{Domain adaptation for moderately similar and dissimilar domains.  Uncurated results for each dataset and generator.}
  \label{fig:fig_A.3_4.Samples}
\end{figure}

\subsubsection{Transferability of StyleDomain Directions to Other Aligned StyleGAN Models}

\textbf{StyleDomain directions of similar domains.} We illustrate the ability of StyleDomain directions optimized for the base generator (e.g. pretrained on FFHQ) to be transferable to other aligned generators fine-tuned to other domains (e.g. MetFaces, Mega, Dogs, etc.). We apply StyleDomain directions that correspond to a wide range of text-based and image-based domains to different aligned generators in \Cref{fig:app_transfer,fig:app_transfer_ext}. We observe that transferability is successful for moderately similar domains and reasonable for dissimilar domains.

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/appendix_transfer.pdf}
  \caption{Examples of transferability of StyleDomain directions optimized for similar domains (text-based and one-shot image-based) to aligned generators fine-tuned to other domains (MetFaces, Mega, Dog, etc.). We observe successful transferability for moderately similar domains and reasonable quality for dissimilar ones.}
  \label{fig:app_transfer}
  \vspace{-0.5cm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/appendix_transfer_ext.pdf}
  \caption{Examples of transferability of StyleDomain directions optimized for similar domains (text-based and one-shot image-based) to aligned generators fine-tuned to other domains (MetFaces, Mega, Dog, etc.). We observe successful transferability for moderately similar domains and reasonable quality for dissimilar ones.}
  \label{fig:app_transfer_ext}
  \vspace{-0.5cm}
\end{figure*}

\FloatBarrier

\textbf{StyleDomain directions of moderately similar domains.}
We illustrate the ability of StyleDomain directions from the fine-tuned models to be transferable to the other aligned generators. We apply these directions from the StyleSpace optimized for Metfaces, Mega and Ukiyo-e domains to the generators that fine-tuned to the moderately similar and dissimilar domains in the Full parameterization.

From Figures~[\ref{fig:app_transfer_distant},~\ref{fig:app_transfer_distant_ext}] we observe that StyleDomain directions can be used to change the style of generated images. Such model modifications work for the moderately similar (most prominently Dog) and for some dissimilar domains (Flowers).

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/Figure_A.5_transfer.01.pdf}
  \caption{Examples of transferability of StyleDomain directions optimized for moderately similar domains (MetFaces, Mega, Ukiyo-e) to aligned generators fine-tuned to other domains (MetFaces, Mega, Dog, etc.). We observe successful transferability for moderately similar domains and reasonable quality for dissimilar ones.}
  \label{fig:app_transfer_distant}

\end{figure*}

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/Figure_A.5_transfer.02.pdf}
  \caption{Examples of transferability of StyleDomain directions optimized for moderately similar domains (MetFaces, Mega, Ukiyo-e) to aligned generators fine-tuned to other domains (MetFaces, Mega, Dog, etc.). We observe successful transferability for moderately similar domains and reasonable quality for dissimilar ones.}
  \label{fig:app_transfer_distant_ext}

\end{figure*}

\FloatBarrier

\subsubsection{Combinations of StyleDomain Directions for Mixed Domain Adaptation}
We present more examples of the StyleDomain directions property to be combined with each other to obtain new mixed domains. In \Cref{fig:app_transfer_combinations,fig:app_transfer_combinations2} we consider all pairwise combinations between different text-based and image-based domains. We observe that new obtained mixed domains have meaningful semantic blending of the corresponding two domains. We also consider combinations of three different domains and provide several examples in \Cref{fig:triple_combinations}. We see that these mixed domains also have a recognisable semantic sense.

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/appendix_domain_sum_v0.pdf}
  \caption{Pairwise combinations of different StyleDomain directions that correspond to text-based or image-based domains. We observe semantically meaningful mixed domains.}
  \label{fig:app_transfer_combinations}
  \vspace{-0.5cm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/appendix_domain_sum_v1.pdf}
  \caption{Pairwise combinations of different StyleDomain directions that correspond to text-based or image-based domains. We observe semantically meaningful mixed domains.}
  \label{fig:app_transfer_combinations2}
  \vspace{-0.5cm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \begin{minipage}{0.5\textwidth}
  \includegraphics[width=\textwidth]{imgs/3pl_morphing/joker_ukiyo-e_pixar.pdf}
  \end{minipage}%
  \begin{minipage}{0.5\textwidth}
  \includegraphics[width=\textwidth]{imgs/3pl_morphing/modigliani_painting_pixar_impressionism_painting.pdf}
  \end{minipage}
  \begin{minipage}{0.5\textwidth}
  \includegraphics[width=\textwidth]{imgs/3pl_morphing/tolkien_elf_pixar_impressionism_painting.pdf}
  \end{minipage}%
  \begin{minipage}{0.5\textwidth}
  \includegraphics[width=\textwidth]{imgs/3pl_morphing/ukiyo-e_botero_werewolf.pdf}
  \end{minipage}
  \begin{minipage}{0.5\textwidth}
  \includegraphics[width=\textwidth]{imgs/3pl_morphing/zombie_pixar_sketch.pdf}
  \end{minipage}%
  \begin{minipage}{0.5\textwidth}
  \includegraphics[width=\textwidth]{imgs/3pl_morphing/tolkien_elf_werewolf_sketch.pdf}
  \end{minipage}
  \begin{minipage}{0.5\textwidth}
  \includegraphics[width=\textwidth]{imgs/3pl_morphing/mermaid_joker_anime.pdf}
  \end{minipage}%
  \begin{minipage}{0.5\textwidth}
  \includegraphics[width=\textwidth]{imgs/3pl_morphing/anastasia_pop_art_modigliani_painting.pdf}
  \end{minipage}
  \caption{Triple combinations of different StyleDomain directions that correspond to text-based or image-based domains. We observe semantically meaningful mixed domains.}
  \label{fig:triple_combinations}
\end{figure*}

\FloatBarrier

\subsubsection{Semantic Editing for Similar Domains}
We demonstrate how StyleDomain directions can be combined with latent transformations for semantic editing.

We utilize the StyleSpace \cite{wu2021stylespace} editing method for "Black hair", "Smile" and "Lipstick" attributes. InterFaceGan \cite{shen2020interpreting} editing method is used for "Yaw", "Rejuvenation", "Aging", "Gender".

\Cref{fig:app_editing_0,fig:app_editing_1} show results of combination of StyleDomain directions for different text-based and image-based domains and editing of mentioned attributes. We see that domain and modification directions are orthogonal, so they are combined successfully.

\begin{figure*}[!h]
  \centering
  \includegraphics[width=0.9\textwidth]{imgs/appendix_editing_v0.pdf}
  \caption{Combination of StyleDomain directions with semantic editing. We observe that they are successfully combined.}
  \label{fig:app_editing_0}
  \vspace{-0.5cm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \includegraphics[width=\textwidth]{imgs/appendix_editing_v1.pdf}
  \caption{Combination of StyleDomain directions with semantic editing. We observe that they are successfully combined.}
  \label{fig:app_editing_1}
  \vspace{-0.5cm}
\end{figure*}

\FloatBarrier

\subsubsection{Semantic Editing for Moderately Similar and Dissimilar Domains}
We analyze the ability to be semantically edited for different parameterizations in the case of moderately similar and dissimilar domains.

We use the StyleSpace \cite{wu2021stylespace} editing method for "Blond Hair", "Black Hair", "Smile", "Lipstick", "Short Hair", "Wavy Hair" and "Gaze" attributes. The StyleFlow \cite{abdal2021styleflow} editing method is used for "Yaw", "Rejuvenation", "Aging", "Gender". Since the models for modifications imply using of images in $1024 \times 1024$ resolution, we train separate models in this resolution from FFHQ1024 checkpoint. We use exactly the same setup as described in Appendix~\ref{app:far_dom} for training these models.

As can be seen from Figure~\ref{fig:app_editing_Mega}, we can utilize existing latent transformations for models fine-tuned with different parameterizations. Notably, we can successfully apply simple style modifications (change of hair color) as well as complex shape transformations (change in age and gender). For more distant domains in Figures~[\ref{fig:app_editing_Metfaces},~\ref{fig:app_editing_Ukiyoe},~\ref{fig:app_editing_Cat_Dog}] some controls become much less pronounced ("Smile") or even inoperable ("Lipstick").

We observe a negligible difference in the quality of modifications between different parameterizations. So, we can conclude that the ability to perform latent transformations mostly depends on the target dataset rather than the choice of parameterization.

\begin{figure*}[!h]
  \centering
  \includegraphics[width=0.9\textwidth]{imgs/Figure_A.5_modifications.Mega.32.pdf}
  \caption{Examples of different parameterizations mixed with semantic editing for Mega. Top row represents attribute names. We can control style (color of hair or lips) as well as the shape (age, gender, hair length and yaw) for all parameterizations.}
  \label{fig:app_editing_Mega}
  \vspace{-0.5cm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \includegraphics[width=0.9\textwidth]{imgs/Figure_A.5_modifications.Metfaces.23.pdf}
  \caption{Examples of different parameterizations mixed with semantic editing for Metfaces. Top row represents attribute names.}
  \label{fig:app_editing_Metfaces}
  \vspace{-0.5cm}
\end{figure*}

\begin{figure*}[!h]
  \centering
  \includegraphics[width=0.96\textwidth]{imgs/Figure_A.5_modifications.Ukiyoe.22.pdf}
  \caption{Examples of different parameterizations mixed with semantic editing for Ukiyo-e. Top row represents attribute names. Most of the transformations are inoperable regardless of the exact choice of parameterization.}
  \label{fig:app_editing_Ukiyoe}
  \vspace{-0.2cm}
\end{figure*}

\begin{figure}[!h]
{
    \begin{tabular}{cc}
        \includegraphics[width=0.48\textwidth]{imgs/Figure_A.5_modifications.Cat.10.pdf} &
        \includegraphics[width=0.48\textwidth]{imgs/Figure_A.5_modifications.Dog.38.pdf} \\
    \end{tabular}
}
\centering
\caption{Examples of different parameterizations mixed with semantic editing for Cat and Dog. Top row represents attribute names. Only working latent transformations are left.}
\label{fig:app_editing_Cat_Dog}
\end{figure}

\section{Practical Benefits of Our Findings}
\label{sec:practical}
\textbf{Immediate practical benefits.} Our analysis and explored properties of aligned StyleGAN2 models deliver the following practical benefits:
\begin{itemize}
    \item we significantly reduce the size of parameterization for domain adaption. For the case of similar domains, we explore StyleDomain directions that have 9 thousand trainable parameters compared to 30 million weights of the Full parameterization. For the case of moderately similar domains, we propose StyleSpace+ that has only half a million parameters.
    \item we discover the transferability of StyleDomain directions to other aligned StyleGAN2 models. It gives the opportunity to apply directions found for one generator to all its child models. We show its wide applicability in \Cref{fig:style_domain_transfer} and in \Cref{app:styledomain}.
    \item we explore the ability of StyleDomain directions to be combined with each other to produce mixed domains. It opens the opportunity to create many interesting combinations of different domains. We demonstrate examples in \Cref{fig:style_domain_mixing} and in \Cref{app:styledomain}.
\end{itemize}

In addition to these applications, we consider two standard tasks from the computer vision and provide more efficient solutions.

\input{s5_i2i}

\textbf{Cross-domain image morphing.} Cross-domain morphing is smooth transition between two images from different domains. This task is known as challenging \cite{aberman2018neural, fish2020image} and it is successfully tackled in the work \cite{wu2021stylealign} using aligned StyleGAN2 models. The idea is to interpolate between aligned generator weights to obtain a smooth transition between domains. We propose more complex image morphing by utilizing the transferability of StyleDomain directions. For example, we can apply a direction that stands for Sketch or Pixar style to Dogs domain to obtain smooth transition between sketchy and pixar-like dog (see \Cref{fig:teaser}). See many examples of such complex cross-domain morphing in \Cref{app:morphing}.

\section{Conclusion}
In this paper, we provide an extensive analysis of the process of StyleGAN domain adaptation. We reveal the sufficient components of the generator for successful adaptation depending on the similarity between the source and target domains. We discover the ability of StyleSpace to change the domain of generated images by StyleDomain directions. Further, we explore and leverage properties of these directions. We believe that our investigation can attract more attention to the exploration of new interesting properties of StyleSpace.

{\small
\bibliographystyle{ieee_fullname}

\section{Introduction}
\label{sec:intro}
Recent years GANs \cite{goodfellow2014generative, karras2019style, karras2020analyzing, karras2021alias, brock2018large} show impressive results in image synthesis and offer many ways of control over generated data. In particular, the state-of-the-art StyleGAN models \cite{karras2019style, karras2020analyzing, karras2021alias}
have many practical applications such as image enhancement \cite{yang2021gan, luo2021time, chan2021glean, wang2021towards}, image editing \cite{jahanian2019steerability, shen2020interpreting, harkonen2020ganspace, tewari2020stylerig, abdal2019image2stylegan, wu2021stylespace, patashnik2021styleclip}, image-to-image translation \cite{pinkney2020resolution, huang2021unsupervised, song2021agilegan, gal2022stylegan} thanks to their high-quality image generation and their latent representation that has rich semantics and disentangled controls for localized meaningful image manipulations. However, it comes at a price as the training of StyleGAN requires a large high-quality dataset that significantly limits its applicability because there are many real-world domains are represented by only few images. The standard approach to deal with this problem is transfer learning, i.e. fine-tuning the model pretrained on the source domain $A$ to the target domain $B$.

There are many domain adaptation methods for StyleGAN \cite{karras2020training, tran2021data, zhao2020differentiable, zhao2020image, liu2020towards, yang2021data, ojha2021few, pinkney2020resolution, alanov2022hyperdomainnet, gal2022stylegan, zhu2021mind, wu2021stylealign} that tackle this problem in different ways depending on the similarity between source $A$ and target $B$ domains. Most of these works implicitly assume that StyleGAN can be adapted to a new domain only if we fine-tune almost all its weights. However, this common wisdom is poorly investigated and verified.
The paper \cite{wu2021stylealign} is the first attempt to perform an in-depth analysis of this question. It examines aligned StyleGAN models, i.e. models when one is fine-tuned from the other to a new domain. They analyze what parts of StyleGAN are changed mostly during domain adaptation. However, they do not touch on the question of what components of StyleGAN are sufficient for adapting the generator.

In this work, we aim to provide a systematic and comprehensive analysis of this question.
Our investigation of the properties of the aligned StyleGAN models consists of several parts. First, in \Cref{sec:importance}, we identify what parts of the StyleGAN are sufficient for its adaptation depending on the similarity between source $A$ and target $B$ domains. We discover that fine-tuning the synthesis convolutional network is not always necessary. In the case of similar $A$ and $B$ domains, the affine layers are sufficient for the adaptation. For dissimilar domains, we can adapt successfully just by adding only one convolutional block from the synthesis network. It suggests investigating the latent space of StyleGAN that is formed by the output of affine layers (i.e., StyleSpace \cite{wu2021stylespace}) to utilize it for domain adaptation.

In the second part of our analysis, in \Cref{sec:styledirections}, we explore directions in the StyleSpace that can adapt StyleGAN to other domains. We first show that such directions exist (and we call it \emph{StyleDomain directions}) and for similar target domains they demonstrate the comparable domain adaptation quality as fine-tuning all weights of StyleGAN. Next, we extend StyleSpace and propose a new latent space \emph{StyleSpace+} that allows adapting even to dissimilar domains. Further, we inspect these StyleDomain directions and discover their surprising properties. For example, we show that we can sum up these directions to obtain a new mixed domain (see \Cref{fig:style_domain_mixing}), and the same directions can be successfully applied to other aligned StyleGAN models (see \Cref{fig:style_domain_transfer}).

Additionally, in \Cref{sec:practical}, we consider practical improvements and applications that can be delivered by our analysis and findings. First, our study allows choosing the optimal parameter space for domain adaptation depending on the type of target domain. Second, we discover and demonstrate different applications of StyleDomain directions such as mixed domains and transferring adaptation between aligned StyleGAN models. Finally, we apply our findings to standard computer vision tasks such as image-to-image translation and cross-domain morphing. In \Cref{sec:practical} we show that image-to-image translation can be solved by significantly less trainable parameters while achieving the same performance. In addition, we consider complex cross-domain image morphing between various domains that can be easily implemented based on our findings.

\section{Introduction}
\label{sec:intro}
Recent years GANs \cite{goodfellow2014generative, karras2019style, karras2020analyzing, karras2021alias, brock2018large} show impressive results in image synthesis and offer many kinds of control over generated data. In particular, the state-of-the-art StyleGAN models \cite{karras2019style, karras2020analyzing, karras2021alias} allow a great number of semantic image manipulations by identifying corresponding directions in different latent spaces ($\mathcal{W}$, $\mathcal{W}+$, or StyleSpace). Between these three latent spaces the StyleSpace provides the most disentangled and interpretable controls for image editing. Such properties of StyleGAN models lead to many practical applications as image enhancement, editing, image-to-image translation, etc.
However, it comes at a price as the training of StyleGAN requires large high-quality dataset that significantly limits its applicability because there are many domains of data that are represented by only few images.

Transfer Learning (TL) is a standard approach to deal with the problem of training GANs with limited data.
In the case of StyleGAN we fine-tune the model pretrained on the source domain $A$ to the target domain $B$.
There are many domain adaptation methods that tackle this problem in different ways depending on the distance between source $A$ and target $B$ domains. Some approaches consider the case when we adapt the StyleGAN to a far domain (e.g. faces $\rightarrow$ cars) and they typically fine-tune all its weights. Other methods deal with the target domains that are similar to the source one. For this case there are works that significantly reduce the parameter space for fine-tuning StyleGAN. However, all these papers focus on a specific aspect of the domain adaptation problem of StyleGAN and do not explore it in a more systematic way except the StyleAlign paper.

StyleAlign is the first attempt to perform in-depth analysis on the transfer learning of StyleGAN. They introduce the term of \emph{aligned models} as the models with the same architecture and one of them (the \emph{child}) is fine-tuned from the other (the \emph{parent}) to a new domain. They focus on aligned StyleGAN models and examine its properties.
While this work provides insightful analysis, some of its conclusions are incomplete or even misleading. For example, they find out that the synthesis convolutional network of StyleGAN is mostly changed during domain adaptation. From this observation one can conclude that this part is the most responsible for the transfer learning, however we show that it is not correct in most cases. It encourages us to revisit this analysis and perform more careful and thorough investigation.

In this work, we aim to provide the systematic and comprehensive analysis on the domain adaptation problem of StyleGAN models and tackle the most important questions that still remain unanswered.
For example, what are main parts of StyleGAN mostly responsible for the tranfer learning to a new domain? How does it depend on the distance between the child and parent domains? Can we adapt the StyleGAN by finding some directions in its latent spaces or do we need necessarily fine-tune its synthesis convolutional network? Can we combine the results of domain adaptations to different domains in one StyleGAN model?

Our investigation of the properties of the aligned StyleGAN models consists of several parts. First, in \Cref{sec:importance}, we identify what parts of the StyleGAN are responsible for its adaptation depending on the similarity between source and target domains. We discover that fine-tuning of the synthesis convolutional network is not always necessary and in some cases the affine layers can be sufficient for the transfer learning (see Figures). It suggests us to investigate the StyleSpace to utilize it for the domain adaptation. So, in the second part of our analysis, in \Cref{sec:styledirections}, we explore directions in the StyleSpace that can adapt StyleGAN to other domains. We first show that such directions exist (and we call it as \emph{StyleDomain directions}) and for similar target domains they demonstrate the comparable domain adaptation quality as the fine-tuning all weights of StyleGAN. Further we examine these StyleDomain directions and discover its surprising properties. For example, we show that we can sum up these directions to obtain new mixed domain, and they can be successfully applied to other aligned StyleGAN model. Overall, our findings contributes to a better understanding of the properties of aligned StyleGAN models.

Additionally, in \Cref{sec:practical}, we consider practical imrovements and applications that can be delivered by our analysis and findings. First, we significantly reduce and simplify the parameter space of the domain adaptation of StyleGAN and show that achieves the same quality as fine-tuning all its weights. Second, we examine different applications of StyleDomain directions such mixed domains and transfering adaptation between aligned StyleGAN models. Finally, we apply our findings to standard image-to-image translation and cross-domain morphing tasks and show that they can be easily solved in our framework.

To summarize, we conduct a comprehensive empirical study on the StyleGAN domain adaptation problem and reveal its many interesting aspects. We first show that StyleSpace has rich semantics not only for image editing but also for domain adaptation. We leverage our findings for a variety of computer vision applications.

\section{Related Work}
\label{sec:related}

\textbf{Latent Spaces of StyleGAN.}
After recent remarkable success of GANs \cite{goodfellow2014generative, karras2019style, karras2020analyzing, karras2021alias, brock2018large} in image synthesis, many works appeared that explore their latent representation for controllable image manipulation. In particular, the latent space of StyleGAN \cite{karras2019style, karras2020analyzing, karras2021alias} has attracted considerable attention. It consists of three levels: (i) the first latent space, $\mathcal{Z}$, is raw random noise (typically Gaussian); (ii) the intermediate latent spaces $\mathcal{W}, \mathcal{W}+$ \cite{abdal2019image2stylegan} are formed by the output of the mapping network; (iii) the last level is StyleSpace, $\mathcal{S}$, \cite{wu2021stylespace} that is spanned by the channel-wise style parameters after affine layers. It has been shown that these latent spaces have rich semantics \cite{karras2019style, karras2020analyzing}, and especially the StyleSpace that demonstrates the most disentangled and localized semantical directions \cite{wu2021stylespace}. In recent years many works have proposed to utilize such appealing properties of the StyleGAN latent spaces for image editing tasks \cite{jahanian2019steerability, shen2020interpreting, harkonen2020ganspace, tewari2020stylerig, abdal2019image2stylegan, wu2021stylespace, patashnik2021styleclip}. To apply these methods for real images it is necessary to inverse them into one of the latent space of StyleGAN which is another task that draws significant attention \cite{abdal2019image2stylegan, karras2020analyzing, guan2020collaborative, pidhorskyi2020adversarial, richardson2021encoding, tov2021designing, zhu2016generative, zhu2020domain}. We should note that all mentioned methods for image manipulation by controlling StyleGAN latent space allow only in-domain editing. In this paper, we show that in StyleSpace there exist such directions that can change the domain of images.

\textbf{Domain Adaptation of StyleGAN.}
Recent years the problem of fine-tuning StyleGAN has generated a great deal of interest as it allows training the state-of-the-art generative model for a domain with few samples. There have appeared many works that tackle this problem in different ways depending on how much the target domain is similar to the source one. Roughly, these methods can be divided into two groups. The first one deals with the case when the target and source domains are completely different (e.g. faces $\rightarrow$ cars, churches, etc.). In contrast, the second group considers the setting of similar domains (e.g. faces $\rightarrow$ stylized faces, painting faces, sketches, etc.). Methods from the first group typically require hundreds or thousands of samples from new domain to adapt faithfully and they leverage data augmentations \cite{karras2020training, tran2021data, zhao2020differentiable, zhao2020image}, or freeze some layers of the discriminator to prevent overfitting \cite{mo2020freeze}, or train the discriminator with auxiliary losses to match the data more accurately \cite{liu2020towards, yang2021data}. In the setting of the second group it is sufficient to have only several samples (up to dozens) from the target domain for the successful adaptation and such approaches utilize another techniques. In particular, they introduce additional regularization terms \cite{tseng2021regularizing, li2020few}, preserve pairwise distances between instances in the source domain via cross-domain consistency loss \cite{ojha2021few}, mix weights of the fine-tuned and the base generators \cite{pinkney2020resolution}, use an auxiliary small network to enhance the training \cite{wang2020minegan}. More advanced methods \cite{gal2022stylegan, zhu2021mind, alanov2022hyperdomainnet} utilize the pretrained CLIP model \cite{radford2021learning} as the vision-language supervision for the text-based adaptation \cite{gal2022stylegan, alanov2022hyperdomainnet} or the one-shot image-based adaptation \cite{zhu2021mind, alanov2022hyperdomainnet}. There are also  methods from the second group that try to reduce the size of the parameter space during fine-tuning process \cite{noguchi2019image, robb2020few, alanov2022hyperdomainnet}. The work \cite{alanov2022hyperdomainnet} utilizes by several orders less parameters and achieves the same expressiveness as models with the full parameter space however it considers only target domains that are similar to the source one.

Although there have been already proposed many methods that deal with the problem of adapting StyleGAN there are still very few works that analyze this process more thoroughly. The paper \cite{wu2021stylealign} is the first attempt to perform such in-depth study. They examine the relationship between the base and fine-tuned StyleGAN generators depending on the similarity between source and target domains (they call them aligned models). In particular, they explore which parts of StyleGAN are mostly changed during fine-tuning and the transferability of the latent semantics between aligned models. However, many important questions remain untouched. In our paper, we provide more systematic and comprehensive analysis that completes and improves results from \cite{wu2021stylealign}.

\section{Importance of Each Part of the StyleGAN for the Domain Adaptation}

\section{Importance of Each Part of the StyleGAN}
\label{sec:importance}
In this section, our goal is to analyze what parts of StyleGAN are important for domain adaptation. Similarly to previous works we specifically focus on the state-of-the-art GAN architecture, StyleGAN2 \cite{karras2020analyzing}. For the source domain, we consider FFHQ \cite{karras2019style} as it is the large high-quality dataset that is suitable for training StyleGAN2 from scratch. For the target one, we test a wide range of different domains that we describe further.

\textbf{StyleGAN2 structure and its main components.} We provide a diagram description of the StyleGAN2 architecture in \Cref{fig:diagram}. It consists of three parts:
\begin{itemize}
    \item \emph{mapping network} $f_M$ that transforms the input noise $z \in \mathcal{Z}$ (typically Gaussian) to the intermediate latent vector $w \in \mathcal{W}$;
    \item \emph{affine layers} $f_1^A, \dots, f_N^A$, each of them takes as input the vector $w$ and maps it to corresponding style vector $s_1 = f_1^A(w), \dots, s_N = f_N^A(w)$. The concatenation of these vectors form the vector from the StyleSpace: $s = (s_1, \dots, s_N) \in \mathcal{S}$;
    \item \emph{synthesis network} that is a composition of modulated convolutions. The weights of each convolution are modulated by the input style vector $s_i$ and applied to the input feature maps. The synthesis network also has tRGB convolutional layers that transform feature maps to RGB images and they also are modulated by corresponding style vectors.
\end{itemize}
Accordingly, the StyleGAN2 $G_{\theta}$ generates from the input noise $z$ the output image $I = G_{\theta}(f_1^A(f_M(z)), \dots, f_N^A(f_M(z)))$ where $\theta$ are weights of the synthesis network.

We will analyze these three components of StyleGAN2 and their impact on the domain adaptation process. It is common wisdom that the most important part for adaptation is the synthesis network while the mapping network and affine layers are responsible mostly for the semantic manipulations within the source domain \cite{wu2021stylealign}. We aim to verify whether such a conception is correct.

In our experiments, we additionally consider the impact of the combination of affine layers and one convolutional block from the synthesis network on domain adaptation. It is a way to probe the intermediate case between affine layers and the synthesis network.

\textbf{Method to analyze the impact of each component.} The paper \cite{wu2021stylealign} has proposed to analyze the impact of each component by resetting its weights in the fine-tuned generator to its pretrained values and assessing the quality of generated images. In our work, we propose another approach: to directly fine-tune only one of these components to explore which ones are sufficient for domain adaptation.

Let us describe our method in more detail. The optimization process for the problem of domain adaptation is the following
\begin{gather}
    \mathcal{L}_D\left(\left\{G_{\theta}(s_1^i, \dots, s_N^i) \right\}_{i=1}^K\right) \rightarrow \min\limits,
\end{gather}
where $\mathcal{L}_D$ is domain adaptation loss that depends on the domain $D$ (we discuss it further) and the samples from the generator $G_{\theta}$, $s^1, \dots, s^K \in \mathcal{S}$ are style vectors obtained from sampled noises $z_1, \dots, z_K \in \mathcal{Z}$, i.e. $s^i = (f_1^A(f_M(z_i)), \dots, f_N^A(f_M(z_i))), \; i = 1, \dots, K$.

Typically the generator $G_{\theta}$ is optimized with respect to all components, i.e.
\begin{gather}
    \mathcal{L}_D\left(\left\{G_{\theta}(s_1^i, \dots, s_N^i) \right\}_{i=1}^K\right) \rightarrow \min\limits_{\theta, f^A, f_M},
\end{gather}
where $f^A = \{f_1^A, \dots, f_N^A\}$.

We propose to investigate settings when we optimize with respect to only one these components. We denote each parameter space as: $\{\theta\}$ -- \emph{SyntConv} parameterization, $\{f^A\}$ -- \emph{Affine} parameterization, $\{f_M\}$ -- \emph{Mapping} parameterization. The case we fine-tune all components of the StyleGAN2 we call \emph{Full} parameterization.

As we discussed above, we propose to consider one additional parameter space as combination of affine layers and one block from synthesis layer with specified spatial resolution. Such block has two convolutional layers with weights $\theta_1, \theta_2 \in \mathbb{R}^{512\times 512\times 3\times 3}$. Instead of fine-tuning all these weights we introduce more compact parameterization as offsets $\Delta \theta_1, \Delta \theta_2$ to these weights that are same across spatial dimensions, i.e. $\Delta \theta_1, \Delta \theta_2 \in \mathbb{R}^{512\times 512\times 1\times 1}$. Such parameterization allows us to reduce the number of trainable parameters almost by an order (from 4,7M to 0,5M parameters). In addition to $\Delta \theta_1, \Delta \theta_2$ we introduce the offsets to the weights $\theta^{tRGB} \in \mathbb{R}^{3\times 512\times 3\times 3}$ of the tRGB convolutional layer of the same block with similar parameterization $\Delta \theta^{tRGB} \in \mathbb{R}^{3\times 512\times 1\times 1}$. Further, we omit such detail about tRGB part in the sake of brevity. So, for this parameterization the optimization procedure has the following form:
\begin{gather}
    \mathcal{L}_D\left(\left\{G_{\theta, \Delta \theta_1, \Delta \theta_2}(s_1^i, \dots, s_N^i) \right\}_{i=1}^K\right) \rightarrow \min\limits_{\Delta \theta_1, \Delta \theta_2, f^A},
\end{gather}
where $G_{\Delta \theta_1, \Delta \theta_2}$ is the generator with weight offsets $\Delta \theta_1, \Delta \theta_2$ for the one block from the synthesis network.

We call such parameter space as \emph{Affine+}. We examine all blocks of the synthesis network to choose for this parameterization. We end up with the block with $64\times64$ resolution as it shows the best performance (see results of this analysis in Appendix).

\begin{table*}[!b]
\centering
\caption{Quality and Diversity metrics \cite{alanov2022hyperdomainnet} for text-based and one-shot image-based domain adaptations with different parameterizations. Affine and StyleSpace parameterizations achieve results comparable with Full parameterization. }
	\label{table:sim_dom_style}
  \begin{tabular}{ llllllllllll }
    \toprule
    & & \multicolumn{2}{c}{\textbf{Botero}} & \multicolumn{2}{c}{\textbf{Sketch}} & \multicolumn{2}{c}{\textbf{The Joker}} & \multicolumn{2}{c}{\textbf{Anastasia (image)}} \\
\cmidrule(lr){3-4} \cmidrule(lr){5-6} \cmidrule(lr){7-8} \cmidrule(lr){9-10}
Parameter Space & Size & Quality & Diversity & Quality & Diversity & Quality & Diversity & Quality & Diversity \\
\midrule\midrule
Full & 30.3M & $0.312$ & $0.228$ & $0.208$ & $0.296$ & $0.246$ & $0.167$ & $0.640$ & $0.276$ \\
SyntConv & 23.6M & $0.311$ & $0.224$ & $0.191$ & $0.292$ & $0.245$ & $0.164$ & $0.683$ & $0.250$ \\
Affine & 4.6M & $0.298$ & $0.221$ & $0.194$ & $0.296$ & $0.244$ & $0.168$ & $0.642$ & $0.269$ \\
Mapping & 2.1M & $0.226$ & $0.115$ & $0.182$ & $0.143$ & $0.246$ & $0.136$ & $0.523$ & $0.202$ \\
StyleSpace & 9K & $0.309$ & $0.23$ & $0.193$ & $0.306$ & $0.242$ & $0.187$ & $0.616$ & $0.296$ \\
    \midrule
    \bottomrule
  \end{tabular}
\end{table*}

\begin{figure}[!b]
  \centering
  \includegraphics[width=0.5\textwidth]{imgs/sim_dom_parts.pdf}
  \caption{Text-based and image-based adaptation for different parameterizations. Affine parameterization yields performance comparable with Full one. This style image is called "Anastasia".}
  \label{fig:sim_dom_parts}
\end{figure}

\textbf{Types of domains.} In our study, we consider three types of domains depending on its similarity to the source domain of realistic faces from FFHQ:
\begin{itemize}
    \item \emph{similar} domains: it has the same geometry of faces and it preserves the identity of the person. It alters only the style of the image. Examples of such domains are text-based adaptations with the text description "Photo in the style of anime (pixar, sketch, etc.)" or one-shot image-based adaptation with different stylized face images (see examples in \Cref{fig:sim_dom_parts}).
    \item \emph{moderately similar} domains: it has face-like form but changes the face geometry and identity in a stronger manner. As examples we consider the following datasets: MetFaces \cite{karras2020training}, Mega cartoon dataset \cite{pinkney2020resolution}, Ukiyo-e faces \cite{pinkney2020resolution}, AFHQ dogs faces and cats faces \cite{choi2020stargan} (see images in \Cref{fig:far_dom_parts}).
    \item \emph{dissimilar} domains: it does not have even face-like structure. We take as examples the following domains: LSUN Car and Church \cite{yu2015lsun}, Flowers \cite{nilsback2006visual} (see images in \Cref{fig:far_dom_parts}).
\end{itemize}

Depending on the type of domain $D$ we use different domain loss function $\mathcal{L}_D$. For similar domains we apply the optimization loss from \cite{gal2022stylegan} in the case of text-based adaptation and another loss from \cite{zhu2021mind} for one-shot image-based adaptation. For moderately similar and dissimilar domains we utilize the fine-tuning procedure from \cite{karras2020training}. For more details about domain adaptation loss functions see \Cref{app:losses}.

To obtain quantitative comparisons in the case of similar domains we use Quality and Diversity metrics that were proposed in the work \cite{alanov2022hyperdomainnet}. For other two types of domains we compute FID metric \cite{heusel2017gans} using the standard protocol from \cite{karras2020training}.

\begin{figure}
  \centering
  \includegraphics[width=0.5\textwidth]{imgs/far_dom_parts.pdf}
  \caption{Domain adaptation for moderately similar and dissimilar domains. Affine+ parameterization produce results on par with Full one even for dissimilar domains (Cat, Church).}
  \label{fig:far_dom_parts}
\end{figure}

\textbf{Analysis for similar domains.} For the analysis we choose different text-based and one-shot image-based domains (see \Cref{app:sim_dom} for the full list and for more details).
In experiments we consider four parameterizations (Full, SyntConv, Affine, Mapping) we discussed above.

We provide qualitative results in \Cref{fig:sim_dom_parts} with quantitative ones in \Cref{table:sim_dom_style}. More results see in \Cref{app:sim_dom}.

We observe that all three parameterizations Full, SyntConv and Affine show comparable performance in terms of both visual quality and objective metrics. The fact that the synthesis network is sufficient for similar domains was clear from the previous work \cite{wu2021stylealign}. However, our finding that affine part is also sufficient is a new and surprising result. It means that we can change the domain of generated images without fine-tuning the synthesis network but just passing the modified style vector that comes from the affine part. Also we observe that the mapping network shows poor visual quality and low diversity in the generated images considering Diversity metric. It indicates that for the successful adaptation it is important to update the style vector from $\mathcal{S}$ rather than the intermediate latent vector from $\mathcal{W}$ space.

For similar domains we do not show results for Affine+ parameterization because Affine already achieves the same quality as the Full parameterization. In our experiments, we observe that Affine+ demonstrates the same performance as Affine.

\begin{table*}[!t]
\centering
\caption{FID scores for domain adaptation with different parameterizations. We observe a significant gap between Affine and Full parametrizations that, however, can be drastically reduced by introducing weight offsets for the single synthesis block.}
	\label{table:one-shot}
  \begin{tabular}{llllllllll}
\toprule
Parameter Space &   Size & Metfaces &     Mega &   Ukiyoe &      Dog &      Cat &      Car &   Church &  Flowers \\
\midrule\midrule
Full               &  30.3M &   $21.9$ &   $81.1$ &   $22.6$ &   $20.3$ &    $7.1$ &   $25.6$ &   $18.1$ &   $17.0$ \\
SyntConv              &  23.6M &   $22.8$ &   $82.5$ &   $24.7$ &   $19.7$ &    $7.2$ &   $25.1$ &   $18.0$ &   $17.4$ \\
Affine+ &   5.1M &   $22.1$ &   $78.2$ &   $26.8$ &   $18.6$ &    $7.0$ &   $43.5$ &   $18.8$ &   $18.0$ \\
Affine            &   4.6M &   $25.9$ &  $108.8$ &   $63.5$ &   $70.1$ &   $27.6$ &  $116.3$ &   $88.3$ &   $42.6$ \\
Mapping           &   2.1M &   $54.9$ &  $137.3$ &  $159.8$ &  $208.2$ &  $226.1$ &  $245.0$ &  $268.6$ &  $236.4$ \\
\midrule\bottomrule
\end{tabular}
  \vspace{-0.5cm}
\end{table*}

\textbf{Analysis for moderately similar domains.}
For this setting as we discussed above, we consider five datasets (MetFaces, Mega, Ukiyo-e faces, AFHQ Dogs and Cats) and the results are provided in \Cref{fig:far_dom_parts} and in \Cref{table:one-shot}. See more results in \Cref{app:far_dom}.

For moderately similar domains we see that results are different compared to similar ones. In particular, we observe that Affine parameterization does not demonstrate the same quality as the full parameterization. It can be seen from degraded visual quality and visible gap in FID metric. However, it is still surprising that the generated images after adaptation have adequate visual apperance considering that we do not fine-tune the synthesis network at all. It is even more surprising that Affine+ parameterization allows removing this performance gap from the Full parameterization both qualitatively and quantitatively. While the number of parameters for the additional block in Affine+ accounts
for only 2 \% of the synthesis network size. It shows that the style vector allows adapting the generator even to more distant domains if we just add the small part of the synthesis network.

SyntConv expectedly achieves results comparable with Full parameterization, and Mapping conversely shows poor quality on all domains.

\textbf{Analysis for dissimilar domains.}
For this setting we take three datasets (Cars, Churchs and Flowers) and the results are provided in \Cref{fig:far_dom_parts} and in \Cref{table:one-shot}. See more results in \Cref{app:far_dom}.

In this case we notice that Affine+ parameterization shows slightly worse results than Full one. The quantitative gap is significant only for Cars domain while for Churches and Flowers it is negligible. It confirms that for the successful adaptation of StyleGAN2 even to dissimilar domains it is sufficient only to fine-tune the small fraction of its all parameters.

The Affine parameterization shows significant degradation in the quality for this case that leads to the conclusion that for dissimilar domains it is important to fine-tune at least some part of the synthesis network. For SyntConv and Mapping parameterization, we observe expected results.

\begin{table*}[!t]
\centering
\caption{FID scores for domain adaptation in $S$ and $S+$. StyleSpace+ parameterization can be competitive for moderately similar domains (Metfaces, Mega).}
	\label{table:far_dom_style}
  \begin{tabular}{llllllllll}
\toprule
Parameter Space &   Size & Metfaces &     Mega &  Ukiyoe &     Dog &     Cat &      Car &  Church & Flowers \\
\midrule\midrule
Full           &  30.3M &   $21.9$ &   $81.1$ &  $22.6$ &  $20.3$ &   $7.1$ &   $25.6$ &  $18.1$ &  $17.0$ \\
StyleSpace$+$ &   0.5M &   $25.3$ &   $83.2$ &  $32.5$ &  $29.4$ &   $9.1$ &   $46.2$ &  $28.7$ &  $22.3$ \\
StyleSpace    &   9.0K &   $30.3$ &  $102.7$ &  $52.0$ &  $75.8$ &  $22.0$ &  $138.7$ &  $66.4$ &  $39.0$ \\
\midrule\bottomrule
\end{tabular}
  \vspace{-0.5cm}
\end{table*}

\section{Exploring Directions in the StyleSpace for the Domain Adaptation of StyleGAN}

\section{StyleSpace for Domain Adaptation}
\label{sec:styledirections}

\begin{figure}[!b]
  \centering
  \includegraphics[width=0.5\textwidth]{imgs/sim_dom_style.pdf}
  \caption{StyleSpace parameterization performs domain adaptation comparable with Full parameterization even with $5000$ times less trained parameters.}
  \label{fig:sim_dom_style}
\end{figure}

\textbf{Exploring directions in the StyleSpace for adapting StyleGAN.} Our findings from the previous section suggest an idea that we can change the domain of generated images by modifying the style vector from the StyleSpace $\mathcal{S}$. To check this hypothesis, we will adapt StyleGAN2 by directly optimizing the direction in $\mathcal{S}$, i.e. during fine-tuning of StyleGAN we will optimize only $\Delta s^D$:
\begin{gather}
    \mathcal{L}_D\left(\left\{G_{\theta}(s_1^i + \Delta s_1^D, \dots, s_N^i + \Delta s_N^D) \right\}_{i=1}^K\right) \rightarrow \min\limits_{\Delta s^D},
\end{gather}
where $\Delta s^D = (\Delta s^D_1, \dots, \Delta s^D_N) \in \mathcal{S}$ is the optimized direction in the $\mathcal{S}$ for adapting the generator $G_{\theta}$ to the domain $D$. We call such directions $\Delta s^D$ as \emph{StyleDomain} directions.

We apply such parameterization to each type of domains and obtain the following results.

For similar domains we provide results in \Cref{fig:sim_dom_style} and in \Cref{table:sim_dom_style} (see more results in \Cref{app:style_results}). We observe that optimizing the StyleDomain direction $\Delta s^D$ achieves the same results both visually and quantitatively as the Full parameterization. It is new and important observation that StyleSpace allows not only image editing within domain but also generating samples from out-of domain.

\begin{figure}[!b]
  \centering
  \includegraphics[width=0.5\textwidth]{imgs/far_dom_style.pdf}
  \caption{Domain adaptation for moderately similar and dissimilar domains. StyleSpace+ parameterization closes the gap in quality for all presented domains despite having $60$ times less trainable parameters.}
  \label{fig:far_dom_style}
\end{figure}

\begin{figure*}[!b]
  \centering
  \includegraphics[width=\textwidth]{imgs/style_domain_transfer.pdf}
  \caption{StyleSpace directions transfer from text-based and image-based domain adaptation to other fine-tuned models. We can successfully transfer style while preserving image content.}
  \label{fig:style_domain_transfer}
\end{figure*}

For experiments with other two types of domains we extend the StyleSpace and introduce new space \emph{StyleSpace+} $\mathcal{S}+$ as the Cartesian product of StyleSpace and the space of weight offsets $\Delta \theta_1, \Delta \theta_2$ to the one block from the synthesis network with resolution $64\times64$ (see \Cref{sec:importance}). Specifically, $\mathcal{S}+ = \mathcal{S}\times \mathbb{R}^{512\times 512\times 1\times 1}\times \mathbb{R}^{512\times 512\times 1\times 1}$, i.e. during fine-tuning process we optimize the vector $\Delta s^D_+ = (\Delta s^D, \Delta \theta_1, \Delta \theta_2) \in \mathcal{S}+$:
\begin{gather}
    \mathcal{L}_D\left(\left\{G_{\theta, \Delta \theta_1, \Delta \theta_2}(s_1^i + \Delta s_1^D, \dots, s_N^i + \Delta s_N^D) \right\}_{i=1}^K\right) \rightarrow \nonumber \\
    \rightarrow \min\limits_{\Delta \theta_1, \Delta \theta_2, \Delta s^D}
\end{gather}

So, for moderately similar and dissimilar domains we provide results  in \Cref{fig:far_dom_style} and in \Cref{table:far_dom_style} (see more results in \Cref{app:style_results}). We observe that the domain adaptation in $\mathcal{S}$ works much worse for this case compared to Full parameterization. However, we should notice that it still shows reasonable quality considering that it has only 9 thousand training parameters which is less by several orders than 30 million parameters in Full parameterization.

Optimizing in $\mathcal{S}+$ allows reducing this performance gap and it is minimal for moderately similar domains. For dissimilar domains we see a noticeable degradation in quality. It means that for this case we need more parameters to fine-tune.

\textbf{Properties of the StyleSpace domain directions.} We show that in $\mathcal{S}$ and  $\mathcal{S}+$ there exist such StyleDomain directions that can adapt the generator to similar and moderately similar domains correspondingly. In this part, we explore its properties in detail.

First, we investigate whether we can transfer StyleDomain directions between aligned StyleGAN2 models. In particular, let us consider the base generator $G_{\theta}$ pretrained on realistic faces and the generator $G_{\theta}^{Dog}$ fine-tuned to domain of dogs in the Full parameterization. We aim to verify whether we could apply StyleDomain directions to $G_{\theta}^{Dog}$ if they were optimized for $G_{\theta}$. Specifically, we conduct the following experiment. At first, we take StyleDomain directions that were optimized for $G_{\theta}$ to adapt it to different text-based and one-shot image-based domains (e.g. Pixar, Anastasia, etc.). Next, we apply these directions to generators that were fine-tuned from $G_{\theta}$ to other domains (e.g. MetFaces, Mega, Dogs, etc.). We provide results of this experiment in \Cref{fig:style_domain_transfer} (see more results in \Cref{app:styledomain}). We see that for moderately similar domains the StyleDomain directions can be successfully transferred. For dissimilar domains the visual quality is less satisfactory but reasonable.

Second, we discover that StyleDomain directions can be combined with each other. In particular, we can consider several directions that correspond to different similar domains and take their linear combination. The resulting direction will adapt the generator to the semantically mixed domain. We provide different examples of such combinations in \Cref{fig:style_domain_mixing} (see more results in \Cref{app:styledomain}).

Finally, we check that StyleDomain directions can be successfully combined with latent controls for image editing. For more detail exploration of this property see in \Cref{app:styledomain}.

\begin{figure}[!t]
  \centering
  \includegraphics[width=0.5\textwidth]{imgs/titan_armin_joker_pixar.pdf}
  \caption{Example of mixing StyleDomain directions. We can combine different directions in order to perform adaptation into a semantically mixed domain.}
  \label{fig:style_domain_mixing}
\end{figure}

redpajama_set_name: RedPajamaArXiv


Former-Solicitor General Florin Hilbay is confident that the senatorial slate of the opposition coalition will win the vote of the people.
"Well, we're very confident given what's happening dun sa ating ekonomiya at sa performance ng administrasyon na to," Hilbay told reporters before filing his candidacy on Wednesday for his senator bid in the May 2019 midterm elections at the Commission on Elections (Comelec) headquarters.
"Sa tingin namin yung mga mamamayan, titingin sa kabila, titingin sa mga bagong boses at boboto sa oposisyon," he added.
The opposition earlier finalized its eight-member senatorial slate for the 2019 polls.


If you like to rock, make your way to New Dodge Lounge this Friday night to check out Trigger. This band plays straight up rock and roll. As they put it, they explore "both heavy and soft sides of the genre". Trigger is made up of Nick James on Vocals and Guitar, Richard Fisher on Bass, Ian Shiemke on Drums and Scott Jasmund on Guitar. Give their song "Hard Times" a listen below.
Friday night Trigger will be joined by Boogie Knights, Glow and The Trip from Indianapolis. Cover for the show is $7 and doors are set to open at 8 p.m. We are also getting some people in the door for free for this one! We are giving away 2 pairs of tickets to Friday's show. To enter to win, send your name to hipindetroit@gmail.com with #trigger in the subject line. We will draw 2 lucky winners on Friday morning and will send them details on how to claim their prize.


I had noticed a cafe in SMX the last time I was here, but since the book fair had food booths inside, and I was too busy hoarding all the books I could get, I wasn't able to even take a peek. This time, I was with my mom, my brother, his girlfriend and our cousin, and once the irritation started bubbling up because of hunger, with no food stalls at the second floor in sight, I suddenly remembered that cafe just to the side of SMX's entrance.
Caffé Bene sounded familiar, then it dawned on me that it was this cafe from Korea. Looking at the cozy ambiance, I was suddenly just focused on getting something to eat and lounging around their quaint little setting of wooden furniture and bookshelves.
I wanted some coffee, but since I was tired, I wanted something cold as well so I opted for this Caramel Macchiato Iced Coffee, which I found better than Starbucks' version.
To say I was hungry was an understatement; my stomach had been growling even before we arrived at SMX that afternoon. The copious booths were able to distract me for a while, though, and since we had plans of eating dinner, my mom and I decided to just share a Clubhouse Sandwich. There were only three little triangle pieces so in my mind, I was screaming for my missing 4th piece. My mom yielded, and gave me two, and then proceeded to munch on some of their chocolate crinkles. Anyway, the sandwich was pretty good; a slice of ham, cheese, omelette, greens, smeared with some honey mustard sort of dressing managed to appease my whining stomach. At the very least, this snack enabled me to once again snake my way through the large halls sans the frown.
One of my cousins told me that their Cream Cheese Gelato was pretty good, too. I gotta try that next time.
What happens to my short reviews of varied food finds? They are merely left to be seen by my Facebook and Instagram followers, but what then of my blog readers?
As I pondered these questions, I decided to include these short reviews on this blog itself so that these little food gems may see the blogosphere's light of day. Short and may or may not be sweet, FRM Bites is all about that particular food I had one afternoon---one I used to deem too short for an official blog post---or a new dish at a restaurant I've already blogged about, or maybe that supermarket find that's just too good to pass up.
Either way, FRM Bites will be a delicious (or literally not) addition to this blog. Feel free to leave a comment or maybe suggest a similar fare that may be better than what I've raved or ranted about.


When you are about to find a clogged drain specialist in Chandler, AZ you should know that there is only one to consider. And that is the one who has worked hard to become good at this, and who has always put the customer first. That one is our company, and when you hire us you won't be afraid of what is going on with your drain, but you will trust us to find out what the problem is quickly. Our company has been working at this kind of thing for a while, and the experience that we have is used during each job that we take on. We do our best, and we make sure that if there is a clog in the drain, that we find it and get it taken care of.
So, you should know that there is no better company that you can hire to be your clogged drain specialist than ours. We are going to make sure that your drain is working well and that there isn't anything to worry about regarding it once we are finished with it. You will just have a drain that is working well, and you will be glad you hired us because we won't even charge you much for the job that we do. We are better than most in all ways, and you will appreciate that.
It is going to make you feel good to have us working as your clogged drain specialist in Chandler, AZ because you will immediately see how cautious and careful we are in the work that we do. We are trained to do it well, and we have experience that helps us, too. We are better than the others who do this work, and you can count on us to fix a clog in your drain.


text:
Themes from 'Edd Kalehoff'
"48 Hours" 1988 – 1996 Theme
In 1988 CBS News started a new kind of news magazine: 48 Hours. The show drew its name, inspiration and original format from a CBS News documentary that was titled 48 Hours on Crack Street. The documentary, which attracted high ratings, was about the drug crisis and how it affected neighborhoods. Like the original documentary, the show focussed on showing events that occurred within a time span of 48 hours, hence the name. The format was phased out in the early 1990s and it became more of a standard news magazine. The series was anchored by Dan Rather. This is the theme they used to open the show.
Edd Kalehoff – 48 Hours theme
https://www.networknewsmusic.com/wp-content/uploads/1988/08/cbs48hours88.mp3
"West 57th" Theme
This hip and slick news magazine was started in 1985 as the 60 Minutes for a younger generation. It was named after the address of the CBS Broadcast Center in New York. The show was fast paced and visually flashy but had essentially the same format as 60 Minutes. The program had no main anchor; the correspondents (like Meredith Vieira and Steve Kroft) would introduce their own stories. Despite its popularity and profitability, West 57th faced a lot of internal criticism. Dan Rather was afraid it took away resources from the CBS Evening News and Don Hewitt saw it as a younger rival to 60 Minutes. The program was cancelled in 1989.
Edd Kalehoff – West 57th
https://www.networknewsmusic.com/wp-content/uploads/1985/08/cbsw57-1.mp3
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redpajama_set_name: RedPajamaCommonCrawl


text:
The best Jackbox Party Pack games on Switch
Who doesn't love a party game? There's little better than the thrill of proving to your friends that you are the superior Pictionary player, punchline writer, or in some cases, t-shirt designer. And of all the party titles currently on the market, ignoring a certain moustachioed plumber and pals, the Jackbox Party Pack series is the perfect combination of side-splitting game design, with all the fun competitive bits for a night sure to make or break those closest to you. Sound like fun? You're damn right.
To help you put on the perfect party, we've put together this list of all the best Jackbox games on Switch. There's an ever-expanding catalogue to choose from, each with its own collection of party games that range from reinventions of classic board games, to trivia-infused murder mystery challenges. Most of these games are super accessible, too, and can be played by up to six to eight players, both locally and online.
Still, if you've already had your fun with Jackbox, why not check out our guide to the best Switch party games, to see what other titles might work into your party planning. Or, if you're looking for something a little more old fashioned but don't want the hassle of getting your pals all in one place, why not browse our picks for the best board games on Switch, and the best mobile board games.
With all that out of the way, it's time to party rock.
The Best Jackbox Party Pack Games
Jackbox Party Pack 3
The third Jackbox collection is our personal favourite, with a varied assortment of hilarious games that differ enough to keep all of your friends out later than they intended. Trivia Murder Party is a lot of fun, with a good balance of skill and chaos to claim victory through answering questions and taking out the leader for good. If you're looking for laughs over the competition, Tee K.O. randomises t-shirt designs and slogans that each player creates into hilarious, if not completely nonsensical, results. There's a good reason that you'll find this third entry at the top of many Jackbox lists.
The Jackbox Party Pack 3 The Jackbox Party Pack 3 Nintendo $24.99 Play Now Network N earns affiliate commission from qualifying sales.
The basic rule for Jackbox Party Packs is that if there's a version of Quiplash included, it's probably one of the better ones, which rings true with Jackbox 7. This collection is perfect if you love your word games, with Quiplash 3, Talking Points, and Blather Round all there to test your linguistic, and comedic, abilities. There is some respite for your artier pals though, as Champ'd Up offers a new spin on Jackbox's drawing games to even out the set. The seventh compendium may lack a little in the chaos department when you compare it to other series titles, but the emphasis on simple-to-grasp wordplay games makes it one of the most accessible Jackbox games to date.
While our first two picks work well in a local setting or online, Jackbox 4 features some of the series' most revealing games, and is best played with friends who you can trust to make fun of the skeletons in your closet. Survive the Internet, in particular, is a celebration of cynicism and sarcasm, which has you creating the funniest headlines, often at the expense of your fellow quip-writers. Jackbox 4's Monster Seeking Monster is still one of the most divisive party games across all series titles, but frankly, we struggle to see the negatives in a monster dating simulator. That is unless you're playing with the family – nobody wants to date their own grandma.
The latest addition brings back an old favourite with quality updates and some new chaotic contenders. Drawful: Animate revives the original Drawful concept with more players and accessibility options, and in all honesty would still rank highly if sold as a single experience. Job Job is a great new addition, allowing you to give the flat-out ridiculous answers you never could in job interviews, while Weapons Drawn offers a bit more of a challenge than the average Jackbox game, but doesn't turn down the hilarity one bit.
Jackbox Party Pack
It might not hold up as well as other early titles, but the original Jackbox needs some kudos for introducing the world to the series' anarchic party games concept. The first collection is where you find Drawful, the Pictionary-type clone that helped to catapult Jackbox to fame. It's still one of the developer's most widely recognised party games, after featuring in multiple streams from popular YouTubers like Markiplier, Let's Play, and more. Admittedly, the other titles present in The Jackbox Party Pack have better versions in more recent games, but this lesson in Jackbox history is still more entertaining than a shower of other party packs from copycat developers.
The Jackbox Party Pack The Jackbox Party Pack Nintendo $24.99 Play Now Network N earns affiliate commission from qualifying sales.
There you have it! While there are other Jackbox Party Pack games, these are the ones we think will really take the lead out of your zeppelin. So now you know how to party in style, why not look for a solo experience to keep you company the morning after, with our lists for the best games like Civilization on Switch, or the best games like Skyrim on Switch.
GM's Cruise secures a permit to charge fares for its driverless robotaxi service in San Francisco, which will operate from 10PM to 6AM on select streets (Rebecca Bellan/TechCrunch)
SERI researchers develop AI tools for screening kidney disease, predicting age using eye photos


The sound which is known as thunder is due simply to the fact that the air traversed by an electric spark, that is, a flash of lightning, is suddenly raised to a very high temperature, and has its volume, moreover considerably increased. The column of gas thus suddenly heated and expanded is sometimes several miles long and as the duration of the flash is not even a millionth of a second, it follows that the noise bursts forth at once from the whole column, though for an observer in any one place it commences where lightning is at least distance.
Electrical charge builds up in a cloud during a thunderstorm. For an explanation of how that happens, at least in our current theory, see last week's post: What Causes Lightning.
A trickle of electricity begins to move downward from the cloud, building up a higher and higher negative charge in the air above the ground. This trickle is known as a leader, and it often branches and turns, seeking the easiest electrically conducting path to the ground.
As the negative leader nears the ground, a positive streamer starts upward building an opposite charge. These two electrical currents, being oppositely charged, will attract and eventually connect. Some observers have reported hearing these leaders and streamers emit a soft crackling/buzzing/electrical type of sound. If you ever hear that in a thunderstorm, duck and run! Lightning is about to strike nearby.
When the leader meets the streamer, the air (normally an electrical insulator) is sufficiently ionized to conduct electricity, and the bulk of the charge flows from the cloud to the ground in a brief burst of high-energy electricity. This is the actual lightning bolt. Its large discharge (temporarily) neutralizes the charge in the cloud and ground.
Lightning bolts are created by electrical potentials of up to 500,000 volts, with currents of up to 200,000 amperes. That's enough electricity to power the entire United States for about 20 minutes.
The bolt itself is only a few centimeters in diameter (judging from holes in buildings created by lightning strikes), but its temperature can reach 20,000 °C — three times hotter than the surface of the Sun.
The bolt quickly heats the air around it, and that air expands explosively, generating a shock wave that propagates outward in all directions at the speed of sound. When it reaches your ear, you hear it as thunder.
That shock wave can often reflect from large objects, like buildings and mountains, creating multiple echoes of the original sound delayed by several seconds.
So basically, the sound of thunder is the same type of thing caused by explosives, except that no combustion is involved. The explosion from lightning is caused purely by the rapid heating of air. And as you probably know, when something is heated it expands. That's what starts the shock wave.
If the time keeps increasing, the storm is moving away from you. That can be a good thing to know.


07. 02. 2019 Please note the notifications about abstract acceptance/rejection will be sent out within first week in March, 2019.
Presenting authors of accepted abstracts will be guaranteed the early registration fee.
A warm welcome to all participants to our NON-INVA meeting and 2nd IFSO-EC Symposium in Lyon. Some of you remember the second IFSO-EC meeting we had in Lyon in 2006, others the regular NON INVA meeting, that we host for 8 years, and has also been combined with Karl Miller's Expert Meeting (formerly in Saalfelden, Austria).
Lyon has a tradition of friendly and casual interactions for a very long time, having played this role since the Roman times, later on during the Renaissance between France and Italy mostly, and above all it is the world capital of gastronomy, more than you need to enjoy your stay here! So go on a strict diet 2 weeks before the meeting... and start again immediately after, you will not be disappointed.
It is my great pleasure to invite you to participate actively in the 2nd IFSO-EC Symposium, taking place in Lyon, May 2nd to 4th 2019.
This IFSO EC symposium will be held on top of the existing, very successful, NON-INVA meeting, organized by Jerome Dargent.
Therefore, one main topic will be new technologies, further minimization of the surgical access and advanced endoscopic procedures.
Our scientific committee, run by Rachel Batterham and Ralph Peterli, is working on a vibrant program including "controversies" and "optimizing outcome after bariatric/metabolic surgery". It is planned to deal with some of the "hottest topics" in bariatric surgery in an Oxford style discussion manner. Touchy subjects like "revisional procedures for weight regain" and "bariatric/metabolic surgery in patients with low BMI" also will be covered.
Don't miss this excellent opportunity to participate in a fantastic congress and meet friends in one of the most history-charged and beautiful cities of Europe!


text:
Watika Lemon
Comics, Anime, SF, and things like that
Crandle the Hat Maker
Review: Demoniacs
April 25, 2018 April 25, 2018 / WatikaLemon
Redemption (Blu-ray)
Two innocent girls fall prey to some pirate 'wreckers,' and then seek demonic help to exact their revenge. It's all typical euro-sleaze horror fare, until the clown shows up.
The film opens with head-shots and descriptions of the four wreckers; the captain who is haunted by his victims (then why does he keep doing it?); the hot woman who takes her clothes off a lot; the second in command, who's plotting against the captain; and the other guy who's playing both sides.
In the case of the captain, his defining character trait (guilt) plays a large roll in the film. It imbues every aspect of his personality, and leads to the ultimate climax. As for the other two male wreckers, their secret plots are never mentioned again after the first two minutes, except in the form of some tepid muttering when the captain makes a scene at a bar. The wrecker woman just nags the others a lot, and provides much of the film's nudity.
The two girls are rather vacant as characters. After some screaming during the initial attack, they never speak again. I suppose that's a legitimate way to depict trauma, but in the context of a story, having main characters that don't emote in any way makes it hard to connect. You can understand their mission on an intellectual level, but there's nothing to grasp onto emotionally.
The girls make their way to some old ruins that are feared by the locals for reasons no one quite remembers. There, they meet a female clown (a classic circus clown with a big red afro), who leads them inside. The girls then free Satan, who has been held prisoner in a dilapidated church, and have sex with him to gain his demonic powers, which seem to be limited to telekinetically nudging statues.
I've never seen a Jean Rollins film before, but from what I gather, this emphasis of style over substance is common, though this film in particular is credited for having a stronger storyline than usual. I guess it does have a storyline, but it's very, very basic. There's nothing flawed in its structure; but it has no detail, nuance, or originality..
The Demoniacs comes to Blu Ray from Redemption films. The video quality is okay. It looks like the source material has aged poorly, leaving the movie washed out and dull. I'm not sure it's significantly better than a good DVD transfer.
Extras include a deleted scene that consists of nothing but film of a ship burning, and two extra sex scenes; one an extension of a scene between the captain and the wrecker woman, and the other involving two recurring background characters.
Demoniacs, Jean Rollin, Review
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Biography – REID, GEORGINA STANLEY – Volume XIV (1911-1920) – Dictionary of Canadian Biography
REID (Reed, Read), GEORGINA STANLEY (Riches), teacher; b. 10 Feb. 1842 in Hull, England, daughter of Mary Wiseman and John Reid, a tailor; m. 19 Oct. 1865 Richard Henry Riches in Port Hope, Upper Canada, and they had two sons; d. 24 Feb. 1915 in Toronto.
Georgina Reid immigrated to Canada with her parents, settling in Port Hope. In 1860 she entered the Toronto Normal School, from which she graduated with a second-class certificate. She then taught privately at her home in Port Hope, evidently with one other teacher. In 1865 she married 40-year-old R. H. Riches of Simcoe County, who had a number of children from a previous marriage. By 1871 they had located in Toronto, where they operated a boarding-house on Adelaide Street West. Sometime after the birth of Georgina's second son in July 1872, Richard left her, but she continued to run the boarding-house.
In September 1875 Georgina joined the Toronto Public School Board and was appointed to teach the fifth division at Niagara Street School. A teacher of some merit, she was promoted to the junior second-book class at Niagara (1876), the senior second-book class at George Street School (1877), and the Orphans' Home school (1878). Some indication of her ability is found in the petition submitted in 1878 by 37 persons that she be reappointed to Niagara Street School. Four years later she brought 24 children from the Orphans' Home before the convention of the Ontario Educational Association for a musical demonstration.
On 6 Sept. 1882 Riches was appointed principal of Palace Street School, at the corner of Front and Cherry streets. The appointment immediately generated controversy. A number of female teachers with higher certificates protested "ill-judged favoritism." In addition, two petitions were presented, one froth the ratepayers of St Lawrence Ward and the other from the parents of children attending the school, against a female head. At the same time, 97 other residents and ratepayers expressed thanks for Riches's appointment. Her selection was ruled unconstitutional, however, when it was discovered that she would be head of a school with a senior third-book class, contrary to board policy that "no teacher shall be promoted to a higher position than that of teacher of the Junior Third Book Class unless they hold a First Class Certificate." One of the most vocal board members, Toronto merchant William Lamb, is said to have stated that it was an "outrage" for her to receive $750 a year; his wife, when she had taught, got only $250. Riches reportedly replied, tartly, that "perhaps it was all she was worth." The issue was resolved on 18 October when, on the authority of inspector James Laughlin Hughes*, Palace Street School was reclassified as a junior third-book school and Riches, whose salary in 1881 had been $475, was reinstated as principal at a salary of $650.
Tradition has it that Riches was the first female principal in Toronto's public schools and the first female principal there to be paid a man's salary. Neither claim is warranted. The school board had 186 teachers and principals on staff in 1882. Most of the 27 men were earning between $850 and $1,100; the majority of the 159 women were receiving between $350 and $500. The highest paid of them, and a principal since 1876, Miss Charlotte Madeline Churchill, was paid $700 in 1882, $50 more than Riches. By the time of Riches's retirement, a female teacher was making, on average, only 48 per cent of a male teacher's salary.
The issue of salaries and sex had arisen in January 1882, when an increase of $25 a year was voted to the female teachers. A number of them petitioned for more, but in March the board invited those who did not agree with the pay schedule to resign. In Toronto, as in other Ontario centres, school trustees recognized that savings could be achieved by hiring a predominantly female teaching force. Typically, men governed as superintendents, trustees, and headmasters, and women were assistant teachers. Riches rose above this situation, but she does not seem to have been involved in the women teachers' association formed in 1885, a circumstance that may have resulted from her obligations as a single parent or from professional differences reflected by her grateful acceptance of the pay raise in 1882. Whatever the reason, she was not alone, since only about half of the eligible women were members of the association.
In 1887 Georgina Riches became principal of Sackville Street School, which replaced Palace Street. She remained there until her retirement in 1912; she had served the public school board for 37 1/2 years. After suffering from rheumatism for three years, she died at her home at 453 Dovercourt Road on 24 Feb. 1915.
Nancy Kiefer
AO, RG 2-12, box 33; RG 2-114, 27 Nov. 1912; RG 22-305, no.29930; RG 80-2-0-29, no.18251. NA, RG 31, C1, 1871, Toronto, St Andrew's Ward, div.2: 12 (mfm. at AO). Toronto Board of Education, Records, Arch., and Museum, Hist. Coll., Toronto Board of Education, minutes, 1882; Vert. files, biog., Georgina Riches. Centennial story: the Board of Education for the city of Toronto, 1850–1950, ed. E. A. Hardy and H. M. Cochrane (Toronto, 1950). Directory, Toronto, 1873–83. E. C. Guillet, In the cause of education: centennial history of the Ontario Educational Association, 1861–1960 (Toronto, 1960). Alison Prentice, "'Friendly atoms in chemistry': women and men at Normal School in mid-nineteenth-century Toronto," in Old Ontario: essays in honour of J. M. S. Careless, ed. David Keane and Colin Read (Toronto, 1990), 285–317; "Themes in the early history of the Women Teachers' Association of Toronto," in Women's paid and unpaid work: historical and contemporary perspectives, ed. Paula Bourne (Toronto, 1985), 97–121. Thomas Riches of Barningham, Norfolk, England, and descendants, 1793–1978, comp. G. H. Riches (Toronto, 1978). Toronto Public School Board, Annual report, 1876–1903, continued as Toronto Board of Education, 1904–12. Women who taught: perspectives on the history of women and teaching, ed. Alison Prentice and M. R. Theobald (Toronto, 1991).
Education – Educators
Nancy Kiefer, "REID, GEORGINA STANLEY," in Dictionary of Canadian Biography, vol. 14, University of Toronto/Université Laval, 2003–, accessed July 16, 2019, http://www.biographi.ca/en/bio/reid_georgina_stanley_14E.html.
Permalink: http://www.biographi.ca/en/bio/reid_georgina_stanley_14E.html
Author of Article: Nancy Kiefer
Title of Article: REID, GEORGINA STANLEY