text:
The Voters Who Elected The Obstructionists
Posted on October 8, 2013 by Isaiah J. Poole
Important to understanding why the federal government has been shut down by a group of unyielding, extremist congressional conservatives is to understand the voters who elected them. That was the goal of a series of focus groups conducted this summer by Democracy Corps, and its report on those focus groups presents an important challenge for progressives.
The "Inside the GOP" report describes a segment of the Republican electorate that is "'worried,' 'discouraged,' 'scared,' and 'concerned' about the direction of the country – and a little powerless to change course." The reasons why would to progressives suggest that these voters live in an alternate universe with little connection to the real world: They think President Obama has succeeded in imposing a "socialist" agenda on the country and that Republicans in Congress have allowed that to happen.
Fed by Fox News and conservative pundits, these voters see a federal government that is using programs like the Affordable Care Act, the Supplemental Nutrition Assistance Program, extended unemployment benefits and other safety net programs to create a dependency class, and thus a loyal political base of support for the Democratic Party agenda.
"While many voters, even some Democrats, question whether Obama is succeeding and getting his agenda done, Republicans think he has won," the report says. "The country may think gridlock has won, particularly during a Republican-led government shut down, but Republicans see a president who has fooled and manipulated the public, lied, and gotten his secret socialist-Marxist agenda done. Republicans and their kind of Americans are losing."
That broad statement, though, needs context. There are clear fissures in the Republican Party that are evident to even casual observers. Christian conservatives are most disturbed by such social changes as marriage equality that they believe are debasing American culture. Tea Party Republicans are more concerned about the size and power of government, and government spending, and include voters with a more libertarian bent than those who identify as Christian conservatives.
The Democracy Corps report also identified a moderate bloc that it says makes up about a quarter of the party that is distinctly uncomfortable with the Tea Party and Christian conservative wings. They share the view that government is too big and inefficient, but they also believe the Republican Party in Washington is at least partly responsible for gridlock and dysfunction.
The report does not offer any political prescriptions; it strives instead to describe "the future battle ahead." There is no suggestion that these voters are "winnable." But this does challenge progressives to present a narrative and a set of values that speaks broadly to the economic anxieties that most Americans share.
The word cloud from the focus groups on the first page of the report serves as a starting point. Americans are "worried," "concerned" and "scared" for good reason, given the economic forces that have held down the middle class and have kept the lowest-income Americans from climbing out of poverty. But recent electoral victories like that of Bill de Blasio in the New York City Democratic mayoral primary, and his current commanding lead over Republican challenger Joseph J. Lhota, demonstrates that a progressive populist narrative that speaks head-on to these anxieties can win broad support.
That narrative explains how we got into this mess, and points the finger at the nexus of big money, failed conservative ideology and, as is now transparently clear, rejection of basic democratic values that has brought us to the precipice of economic and political catastrophe.
That narrative also sets forth the vision that if we get the government back into the business of growing the economy, a sense of hope will also grow, and the values of generosity and fairness that have helped fuel America's most progressive strides forward can once again prevail.
There has always been an extreme right fringe in American politics. We can't hope to make it disappear, but we can rob it of its power.
Posted in: Conservatism, Progressive Vision


Be Content. Our debut album, out now.
This album is about constantly not fitting in and having that weirdness build a barrier between yourself and those around you. I know it sounds proper lame but on days like today it leaves us so overwhelmed to know that there's so many people supporting us. Thank you all so much. Now buy the album stupid.
Our new single, Pro Procrastinator, is out everywhere now.
The Be Content Uk Tour. April 2019.
We will be hitting the road for our biggest UK tour in April 2019.


If you are standing in home depot around my current location paint section now, you may be wondering which of the two brands to pick from, Behr or Glidden. You might even be taking into consideration that third option, Ralph Lauren Paint.
Paint contractors may say you to skip home depot around my current location the complete and then again purchase from a standalone amassing like Sherwin-Williams or Benjamin Moore. But if you're on a budget, or are working upon a smaller project in imitation of your kids' room or a guest bathroom, home depot around my current location paints are perfectly acceptable.
One thing that most homeowners don't know is that home depot around my current location has a line of paint designed for contractors called Behr PRO. Introduced in April 2015, Behr PRO offers paint at steeply lower prices than its consumer line.
Just taking into account any new consumer product, buying in bulk doesn't always pay off. since you grab that allegedly cheaper 5-gallon bucket, price it out on a per-gallon basis and look if single gallons might be cheaper. For example, you may locate that one gallon of interior paint is $16.68. You may be tempted to grab the 5-gallon bucket which retails for $87.50, but if you calculate the price per gallon you'll locate it is actually more expensive: $17.50. Prices may modify close you, suitably always get the math to find your best deal.How to Get a Paint Sample Off a Wall?
If you want to paint your walls a alternative color to come to an understanding a specific room in your house, you'll obsession to get an true get along with from the paint store. Unless you have a photographic memory, you likely won't be accomplished to locate an precise acquiesce without using a sample. Therefore, you habit to surgically remove a paint sample from the wall following the color you want to match.
Locate an obscure section of the wall, such as astern a fragment of furniture, and hold the heat gun two inches away from the wall. Apply the heat — heat loosens the hold paint has to wood and drywall. different the heat gun in a small circle; reach not sustain it stationary for more than ten seconds, or you risk scorching the wall.
Watch the paint. later than it begins to bubble, slant off the heat and get your scraper.


A Level Subject Choices – Why Choose Maths?
At this time of year, with GCSE exams now over, it will soon be time to make your final A Level subject choices.
Here are 10 reasons why you should consider Maths when making your A Level subject choices.
One of the best reasons to choose an A Level subject is the fact that you enjoy the subject.
Employers are keen on candidates who have A Level Maths.
A Level Maths develops your ability to solve problems in the wider world as well as the mathematical world.
Studying A Level Maths encourages your ability to think logically.
Maths is the language of the universe.
If you can master Maths at A Level, everything else will seem easier.
If you are thinking of studying Business Studies at university, you will find A Level Maths extremely useful, even if the course you are interested in does not list it as an essential.
If you want to study computers or computer sciences at university, you won't find it easy without A Level Maths.
You will often hear mathematicians talk about the beauty of maths. It's not until you study maths at A Level or higher that you begin to really appreciate the beautiful nature of maths.
This might surprise you but research has shown that people with A Level Maths tend to earn between 7% and 10% more than their peers who do not have a Maths A level. The increased earnings does not depend on having an A grade either, simply having an E grade Maths A Level or higher will significantly increase your earnings potential.
The full text of the original article by Anna Vignoles, published in CentrePiece, the magazine of the Centre for Economic Performance at LSE, is available from LSE.


text:
\section{Introduction}
Person re-identification (re-ID) aims at retrieving the same persons' images from images captured by different cameras.
In recent years, person re-ID datasets with increasing numbers of images
were proposed to facilitate the research along this direction.
All the datasets require time-consuming annotations and are keys for re-ID performance improvements.
However, even with such large-scale datasets, for person images from a new camera system, the person re-ID models trained on existing datasets generally show evident performance drops because of the domain gaps. Unsupervised Domain Adaptation (UDA) is therefore proposed to adapt the model trained on the source image domain (dataset) with identity labels to the target image domain (dataset) with no identity annotations.

State-of-the-art UDA methods~\citep{song2018unsupervised,zhang2019self,yang2019selfsimilarity} for person re-ID group unannotated images with clustering algorithms and train the network with clustering-generated pseudo labels.
Although the pseudo label generation and feature learning with pseudo labels are conducted alternatively to refine the pseudo labels to some extent,
the training of the neural network is still substantially hindered by the inevitable label noise.
The noise derives from the limited transferability of source-domain features, the unknown number of target-domain identities, and the imperfect results of the clustering algorithm.
The refinery of noisy pseudo labels has crucial influences to the final performance, but is mostly ignored by the clustering-based UDA methods.

\begin{figure}[tb]
\begin{center}
       \includegraphics[width=0.6\linewidth]{intro.pdf}
\end{center}
\vspace{-10pt}
   \caption{
   Person image $A_1$ and $A_2$ belong to the same identity while $B$ with similar appearance is from another person.
   However, clustering-generated pseudo labels in state-of-the-art Unsupervised Domain Adaptation (UDA) methods contain much noise that hinders feature learning.
   We propose pseudo label refinery with on-line refined soft pseudo labels to effectively mitigate the influence of noisy pseudo labels and improve UDA performance on person re-ID.}
\label{fig:intro}
\end{figure}

To effectively address the problem of noisy pseudo labels in clustering-based UDA methods \citep{song2018unsupervised,zhang2019self,yang2019selfsimilarity} (Figure~\ref{fig:intro}),
we propose an unsupervised Mutual Mean-Teaching (MMT) framework to effectively perform pseudo label refinery by optimizing the neural networks under the joint supervisions of off-line refined hard pseudo labels and on-line refined soft pseudo labels.
Specifically, our proposed MMT framework provides robust soft pseudo labels in an on-line peer-teaching manner,
which is inspired by the {teacher-student} approaches \citep{tarvainen2017mean,zhang2018deep} to simultaneously train two same networks. The networks gradually capture target-domain data distributions and thus refine pseudo labels for better feature learning. To avoid training error amplification, the temporally average model of each network is proposed to produce reliable soft labels for supervising the other network in a collaborative training strategy.
By training peer-networks with such on-line soft pseudo labels on the target domain, the learned feature representations can be iteratively improved to provide more accurate soft pseudo labels, which, in turn, further improves the discriminativeness of learned feature representations.

The classification and triplet losses are commonly adopted together to achieve state-of-the-art performances in both fully-supervised~\citep{luo2019bag} and unsupervised~\citep{zhang2019self,yang2019selfsimilarity} person re-ID models.
However, the conventional triplet loss~\citep{hermans2017defense} cannot work with such refined soft labels.
To enable using the triplet loss with soft pseudo labels in our MMT framework, we propose a novel soft softmax-triplet loss so that the network can benefit from softly refined triplet labels.
The introduction of such soft softmax-triplet loss is also the key to the superior performance of our proposed framework.
Note that the collaborative training strategy on the two networks is only adopted in the training process. Only one network is kept in the inference stage without requiring any additional computational or memory cost.

The contributions of this paper could be summarized as three-fold.
(1) We propose to tackle the label noise problem in state-of-the-art clustering-based UDA methods for person re-ID, which is mostly ignored by existing methods but is shown to be crucial for achieving superior final performance.
{The proposed Mutual Mean-Teaching (MMT) framework is designed to provide more reliable soft labels.}
(2) Conventional triplet loss can only work with hard labels. To enable training with soft triplet labels for mitigating the pseudo label noise, we propose the soft softmax-triplet loss to learn more discriminative person features.
(3)
The MMT framework shows exceptionally strong performances on all UDA tasks of person re-ID.
Compared with state-of-the-art methods, it leads to significant improvements of \textbf{14.4\%}, \textbf{18.2\%}, \textbf{13.4\%}, \textbf{16.4\%} mAP on Market-to-Duke, Duke-to-Market, Market-to-MSMT, Duke-to-MSMT re-ID tasks.

\section{Related Work}
\noindent\textbf{Unsupervised domain adaptation (UDA) for person re-ID.}
UDA methods have attracted much attention because their capability of saving the cost of manual annotations.
There are three main categories of methods.
The first category of clustering-based methods maintains state-of-the-art performance to date.
\citep{fan2018unsupervised} proposed to alternatively assign labels for unlabeled training samples and optimize the network with the generated targets.
\citep{lin2019aBottom} proposed a bottom-up clustering framework with a repelled loss.
\citep{yang2019selfsimilarity} introduced to assign hard pseudo labels for both global and local features.
However,
the training of the neural network was substantially hindered by the noise of the hard pseudo labels generated by clustering algorithms, which was mostly ignored by existing methods.
The second category of methods learns domain-invariant features from style-transferred source-domain images.
SPGAN~\citep{deng2018image} and PTGAN~\citep{wei2018person}
transformed source-domain images to match the image styles of the target domain while maintaining the original person identities. The style-transferred images and their identity labels were then used to fine-tune the model.
HHL~\citep{zhong2018generalizing}
learned camera-invariant features with camera style transferred images.
However, the retrieval performances of these methods deeply relied on the image generation quality, and they did not explore the complex relations between different samples in the target domain.
The third category of methods attempts on optimizing the neural networks with soft labels for target-domain samples by computing the similarities with reference images or features.
ENC~\citep{zhong2019invariance} assigned soft labels by saving averaged features with an exemplar memory module.
MAR~\citep{yu2019unsupervised} conducted multiple soft-label learning by comparing with a set of reference persons.
However, the reference images and features might not be representative enough to generate accurate labels for achieving advanced performances.

\noindent\textbf{Generic domain adaptation methods for close-set recognition.}
Generic domain adaptation methods
learn features that can minimize the differences between data distributions of source and target domains.
Adversarial learning based methods~\citep{zhang2018collaborative,tzeng2017adversarial,ghifary2016deep,bousmalis2016domain,tzeng2015simultaneous} adopted a domain classifier to dispel the discriminative domain information from the learned features in order to reduce the domain gap.
There also exist methods~\citep{tzeng2014deep,long2015learning,yan2017mind,saito2018maximum,ghifary2016deep} that minimize the Maximum Mean Discrepancy (MMD) loss between source- and target-domain distributions.
However, these methods assume that the classes on different domains are shared, which is not suitable for unsupervised domain adaptation on person re-ID.

\noindent\textbf{Teacher-student models} have been widely studied in semi-supervised learning methods and knowledge/model distillation methods.
The key idea of {teacher-student} models is to create consistent training supervisions for labeled/unlabeled data via different models' predictions.
Temporal ensembling~\citep{laine2016temporal} maintained an exponential moving average prediction for each sample as the supervisions of the unlabeled samples, while the mean-teacher model~\citep{tarvainen2017mean} averaged model weights at different training iterations to create the supervisions for unlabeled samples.
Deep mutual learning~\citep{zhang2018deep} adopted a pool of student models instead of the teacher models by training them with supervisions from each other.
However, existing methods with {teacher-student} mechanisms are mostly designed for close-set recognition problems, where both labeled and unlabeled data share the same set of class labels and could not be directly utilized on unsupervised domain adaptation tasks of person re-ID.

\noindent\textbf{Generic methods for handling noisy labels}
can be classified into four categories.
Loss correction methods \citep{patrini2017making,vahdat2017toward,xiao2015learning} tried to model the noise transition matrix, however, such matrix is hard to estimate in real-world tasks, \textit{e}.\textit{g}.  unsupervised person re-ID with noisy pseudo labels obtained via clustering algorithm.
\citep{veit2017learning,lee2018cleannet,li2017learning,Han_2019_ICCV} attempted to correct the noisy labels directly, while the clean set required by such methods limits their generalization on real-world applications.
Noise-robust methods designed robust loss functions against label noises, for instance, Mean Absolute Error (MAE) loss \citep{ghosh2017robust}, Generalized Cross Entropy (GCE) loss \citep{zhang2018generalized} and Label Smoothing Regularization (LSR) \citep{szegedy2016rethinking}.
However, these methods did not study how to handle the triplet loss with noisy labels, which is crucial for learning discriminative feature representations on person re-ID.
The last kind of methods which focused on refining the training strategies is mostly related to our method.
Co-teaching \citep{han2018co} trained two collaborative networks and conducted noisy label detection by selecting on-line clean data for each other, Co-mining \citep{wang2019co} further extended this method on the face recognition task with a re-weighting function for Arc-Softmax loss \citep{deng2019arcface}.
However, the above methods are not designed for the open-set person re-ID task and could not achieve state-of-the-art performances under the more challenge unsupervised settings.

\section{Proposed Approach}
We propose a novel Mutual Mean-Teaching (MMT) framework for tackling the problem of noisy pseudo labels in clustering-based Unsupervised Domain Adaptation (UDA) methods. The label noise has important impacts to the domain adaptation performance but was mostly ignored by those methods.
Our key idea is to conduct pseudo label refinery in the target domain by optimizing the neural networks with off-line refined hard pseudo labels and on-line refined soft pseudo labels in a collaborative training manner.
In addition, the conventional triplet loss cannot properly work with soft labels. A novel soft softmax-triplet loss is therefore introduced to better utilize the softly refined pseudo labels.
Both the soft classification loss and the soft softmax-triplet loss work jointly to achieve optimal domain adaptation performances.

Formally, we denote the source domain data as ${\mathbb{D}}_s = \{({\bm{x}}_i^s, {\bm{y}}_i^s) |_{i=1}^{N_s}\}$, where ${\bm{x}}_i^s$ and ${\bm{y}}_i^s$ denote the $i$-th training sample and its associated person identity label, $N_s$ is the number of images, and $M_s$ denotes the number of person identities (classes) in the source domain. The $N_t$ target-domain images are denoted as ${\mathbb{D}}_t = \{{\bm{x}}_i^t|_{i=1}^{N_t}\}$, which are not associated with any ground-truth identity label.
\subsection{Clustering-based UDA Methods Revisit}
\label{sec:revisit}
\vspace{-5pt}

State-of-the-art UDA methods \citep{fan2018unsupervised,lin2019aBottom,zhang2019self,yang2019selfsimilarity} follow a similar general pipeline. They generally pre-train a deep neural network $F(\cdot|{\bm{\theta}})$ on the source domain, where ${\bm{\theta}}$ denotes current network parameters,
and the network is then transferred to learn from the images in the target domain.
The source-domain images' and target-domain images' features encoded by the network are denoted as $\{F({\bm{x}}_i^s|{\bm{\theta}})\}|_{i=1}^{N_s}$ and $\{F({\bm{x}}_i^t|{\bm{\theta}})\}|_{i=1}^{N_t}$ respectively.
As illustrated in Figure~\ref{fig:fw} (a),
two operations are alternated to gradually fine-tune the pre-trained network on the target domain.
(1) The target-domain samples are grouped into pre-defined $M_t$ classes by clustering the features $\{F({\bm{x}}_i^t|{\bm{\theta}})\}|_{i=1}^{N_t}$ output by the current network.
Let $\tilde{{\bm{y}}}^t_i$ denotes the pseudo label generated for image ${\bm{x}}^t_i$.
(2) The network parameters ${\bm{\theta}}$ and a learnable target-domain classifier $C^t: {\bf f}^t  \rightarrow  \{1, \cdots, M_t \}$
are then optimized with respect to an identity classification (cross-entropy) loss $\mathcal{L}^t_{id}({\bm{\theta}})$ and a triplet loss~\citep{hermans2017defense} $\mathcal{L}^t_{tri}({\bm{\theta}})$ in the form of,
{\small
\begin{align}
        \mathcal{L}_{id}^t({\bm{\theta}}) &= \frac{1}{N_t} \sum_{i=1}^{N_t} \mathcal{L}_{ce} \left( C^t(F({\bm{x}}_i^t|{\bm{\theta}})), \tilde{{\bm{y}}}^t_i  \right),
\label{eq:target_id}
        \\
        \mathcal{L}_{tri}^t({\bm{\theta}}) &= \frac{1}{N_t} \sum_{i=1}^{N_t}
\max \left(0, ||F({\bm{x}}_i^t|{\bm{\theta}}) - F({\bm{x}}_{i,p}^t|{\bm{\theta}})|| + m
 - ||F({\bm{x}}_i^t|{\bm{\theta}}) - F({\bm{x}}_{i,n}^t|{\bm{\theta}})|| \right),
\label{eq:target_triplet}
\end{align}}%

\noindent where $||\cdot||$ denotes the $L^2$-norm distance,
subscripts $_{i,p}$ and $_{i,n}$ indicate the hardest positive and hardest negative feature index in each mini-batch for the sample ${\bm{x}}_i^t$, and $m=0.5$ denotes the triplet distance margin.
Such two operations, pseudo label generation by clustering and feature learning with pseudo labels, are alternated until the training converges.
However, the pseudo labels generated in step (1) inevitably contain errors due to the imperfection of features as well as the errors of the clustering algorithms, which hinder the feature learning in step (2). To mitigate the pseudo label noise, we propose the Mutual Mean-Teaching (MMT) framework together with a novel soft softmax-triplet loss to conduct the pseudo label refinery.

\begin{figure}[tb]
\begin{center}
	\includegraphics[width=1.0\linewidth]{fw3.pdf}
\end{center}
\vspace{-10pt}
\caption{
(a) The pipeline for existing clustering-based UDA methods on person re-ID with noisy hard pseudo labels.
(b) Overall framework of the proposed Mutual Mean-Teaching (MMT) with
two collaborative networks jointly optimized under the supervisions of off-line refined hard pseudo labels and on-line refined soft pseudo labels. A soft identity classification loss and a novel soft softmax-triplet loss are adopted.
(c) One of the average models with better validated performance is adopted for inference as average models perform better than models with current parameters.}
\label{fig:fw}
\end{figure}

\subsection{Mutual Mean-Teaching (MMT) Framework}
\vspace{-5pt}

\subsubsection{Supervised Pre-training for Source Domain}
\vspace{-5pt}
UDA task on person re-ID aims at transferring the knowledge from a pre-trained model on the source domain to the target domain.
A deep neural network is first pre-trained on the source domain. Given the training data ${\mathbb{D}}_s$, the network is trained to model a feature transformation function $F(\cdot|{\bm{\theta}})$ that transforms each input sample ${\bm{x}}_i^s$ into a feature representation $F({\bm{x}}_i^s|{\bm{\theta}})$.
Given the encoded features, the identification classifier $C^s$ outputs an $M_s$-dimensional probability vector to predict the identities in the source-domain training set.
The neural network is trained with a classification loss $\mathcal{L}^s_{id}({\bm{\theta}})$ and a triplet loss $\mathcal{L}^s_{tri}({\bm{\theta}})$ to separate features belonging to different identities.
The overall loss is therefore calculated as
{\small
\begin{align}
                & \mathcal{L}^s({\bm{\theta}}) = \mathcal{L}^s_{id}({\bm{\theta}}) + \lambda^s \mathcal{L}_{tri}^s({\bm{\theta}}),
        \label{eq:source}
\end{align}}%
where $\mathcal{L}^s_{id}({\bm{\theta}})$ and $\mathcal{L}^s_{tri}({\bm{\theta}})$ are defined similarly to \eqref{eq:target_id} and \eqref{eq:target_triplet} but with ground-truth identity labels $\{{{\bm{y}}_i^s}|_{i=1}^{N_s}\}$, and $\lambda^s$ is the parameter weighting the two losses.

\subsubsection{Pseudo Label Refinery with On-line Refined Soft Pseudo Labels}
\label{sec:soft}
\vspace{-5pt}

Our proposed MMT framework is based on the clustering-based UDA methods with off-line refined hard pseudo labels as introduced in Section \ref{sec:revisit}, where the pseudo label generation and refinement are conducted alternatively. However, the pseudo labels generated in this way are hard (\textit{i}.\textit{e}.,  they are always of $100\%$ confidences) but noisy. In order to mitigate the pseudo label noise, apart from the off-line refined hard pseudo labels, our framework further incorporates on-line refined soft pseudo labels (\textit{i}.\textit{e}.,  pseudo labels with $<100\%$ confidences) into the training process.

Our MMT framework generates soft pseudo labels by collaboratively training two same networks with different initializations. The overall framework is illustrated in Figure \ref{fig:fw} (b). The pseudo classes are still generated the same as those by existing clustering-based UDA methods, where each cluster represents one class. In addition to the hard and noisy pseudo labels, our two collaborative networks also generate on-line soft pseudo labels by network predictions for training each other. The intuition is that, after the networks are trained even with hard pseudo labels, they can roughly capture the training data distribution and their class predictions can therefore serve as soft class labels for training. However, such soft labels are generally not perfect because of the training errors and noisy hard pseudo labels in the first place.
To avoid two networks collaboratively bias each other, the past temporally average model of each network instead of the current model is used to generate the soft pseudo labels for the other network. Both off-line hard pseudo labels and on-line soft pseudo labels are utilized jointly to train the two collaborative networks. After training, only one of the past average models with better validated performance is adopted for inference (see Figure \ref{fig:fw} (c)).

We denote the two collaborative networks as feature transformation functions $F(\cdot| {\bm{\theta}}_{1})$ and $F(\cdot| {\bm{\theta}}_{2})$, and denote their corresponding pseudo label classifiers as $C_{1}^t$ and $C_{2}^t$, respectively.
To simultaneously train the coupled networks, we feed the same image batch to the two networks but with separately random erasing, cropping and flipping. Each target-domain image can be denoted by ${\bm{x}}_{i}^t$ and ${{\bm{x}}'}_{i}^{t}$ for the two networks, and their pseudo label confidences can be predicted as $C_1^t(F({\bm{x}}^t_i|{\bm{\theta}}_1))$ and $C_2^t(F({{\bm{x}}'}^t_i|{\bm{\theta}}_2))$.
One na\"ive way to train the collaborative networks is to directly utilize the above pseudo label confidence vectors as the soft pseudo labels for training the other network. However, in such a way, the two networks' predictions might converge to equal each other and the two networks lose their output independences. The classification errors as well as pseudo label errors might be amplified during training.
In order to avoid error amplification, we propose to use the temporally average model of each network to generate reliable soft pseudo labels for supervising the other network. Specifically, the parameters of the temporally average models of the two networks at current iteration $T$ are denoted as $E^{(T)}[{\bm{\theta}}_1]$ and $E^{(T)}[{\bm{\theta}}_2]$ respectively, which can be calculated as
{\small
\begin{align}
E^{(T)}[{\bm{\theta}}_{1}] & = \alpha E^{(T-1)}[{\bm{\theta}}_{1}] +(1-\alpha) {\bm{\theta}}_{1},  \nonumber \\
E^{(T)}[{\bm{\theta}}_{2}] & =\alpha E^{(T-1)}[{\bm{\theta}}_{2}] +(1-\alpha) {\bm{\theta}}_{2},
\label{eq:emb}
\end{align}}%
where $E^{(T-1)}[{\bm{\theta}}_{1}]$, $E^{(T-1)}[{\bm{\theta}}_{2}]$ indicate the temporal average parameters of the two networks in the previous iteration $(T-1)$, the initial temporal average parameters are $E^{(0)}[{\bm{\theta}}_1] = {\bm{\theta}}_1$, $E^{(0)}[{\bm{\theta}}_2] = {\bm{\theta}}_2$, and $\alpha$ is the ensembling momentum to be within the range $[0,1)$. The robust soft pseudo label supervisions are then generated by the two temporal average models as $C_1^t(F({\bm{x}}^t_i|E^{(T)}[{\bm{\theta}}_{1}]))$ and $C_2^t(F({{\bm{x}}'}^t_i|E^{(T)}[{\bm{\theta}}_{2}]))$ respectively. The soft classification loss for optimizing ${\bm{\theta}}_1$ and ${\bm{\theta}}_2$ with the soft pseudo labels generated from the other network can therefore be formulated as
{\small
\begin{align}
        \mathcal{L}_{sid}^t({\bm{\theta}}_1|{\bm{\theta}}_2) =  - \frac{1}{N_t} \sum_{i=1}^{N_t}& \left( C_2^t(F({{\bm{x}}'}^t_i|E^{(T)}[{\bm{\theta}}_{2}])) \cdot \log C_1^t(F({\bm{x}}^t_i|{\bm{\theta}}_1)) \right),
        \nonumber    \\
        \mathcal{L}_{sid}^t({\bm{\theta}}_2|{\bm{\theta}}_1) =  - \frac{1}{N_t} \sum_{i=1}^{N_t}& \left( C_1^t(F({\bm{x}}^t_i|E^{(T)}[{\bm{\theta}}_{1}])) \cdot \log  C_2^t(F({{\bm{x}}'}^t_i|{\bm{\theta}}_2)) \right).
        \label{eq:soft_id}
\end{align}}%

The two networks' pseudo-label predictions
are better dis-related by using other network's past average model to generate supervisions and can therefore better avoid error amplification.

Generalizing classification cross-entropy loss to work with soft pseudo labels has been well studied \citep{hinton2015distilling}, \citep{muller2019does}. However, optimizing triplet loss with soft pseudo labels poses a great challenge as no previous method has investigated soft labels for triplet loss. For tackling the difficulty, we propose to use softmax-triplet loss, whose hard version is formulated as
{\small
\begin{align}
        \mathcal{L}^t_{tri}&({\bm{\theta}}_1) = \frac{1}{N_t} \sum_{i=1}^{N_t} \mathcal{L}_{bce}\biggr(\mathcal{T}_i({\bm{\theta}}_1) ,\bm{1}\biggr),
        \label{eq:hard_tri}
\end{align}}%
where
{ \small
\begin{align}
        \mathcal{T}_i({\bm{\theta}}_1) = \frac{\exp(\| F({\bm{x}}^t_i|{\bm{\theta}}_1) - F({\bm{x}}^t_{i,n}|{\bm{\theta}}_1)\|)}{\exp(\|F({\bm{x}}^t_i|{\bm{\theta}}_1) - F({\bm{x}}^t_{i,p}|{\bm{\theta}}_1)\|) + \exp(\| F({\bm{x}}^t_i|{\bm{\theta}}_1) - F({\bm{x}}^t_{i,n}|{\bm{\theta}}_1)\|)}.
        \label{eq:T}
\end{align}}%

Here
$\mathcal{L}_{bce}(\cdot,\cdot)$ denotes the binary cross-entropy loss,
$F({\bm{x}}^t_i|{\bm{\theta}}_1)$ is the encoded feature for target-domain sample ${\bm{x}}^t_i$ by network $1$, the subscripts $_{i,p}$ and $_{i,n}$ denote sample ${\bm{x}}_i^t$'s hardest positive and negative samples in the mini-batch,
$\|F({\bm{x}}^t_i|{\bm{\theta}}_1) - F({\bm{x}}^t_{i,p}|{\bm{\theta}}_1)\|$ is the $L^2$-norm distance between sample ${\bm{x}}^t_i$ and its positive sample ${\bm{x}}^t_{i,p}$ to measure their similarity, and ``$\bm{1}$'' denotes the ground-truth that
the positive sample ${\bm{x}}^t_{i,p}$ should be closer to the sample ${\bm{x}}^t_{i}$ than its negative sample ${\bm{x}}^t_{i,n}$.
Given the two collaborative networks, we can utilize the one network's past temporal average model to generate soft triplet labels for the other network with the proposed soft softmax-triplet loss,
{\small
\begin{align}
        \mathcal{L}^t_{stri}&({\bm{\theta}}_1|{\bm{\theta}}_2) = \frac{1}{N_t} \sum_{i=1}^{N_t} \mathcal{L}_{bce}\biggr(\mathcal{T}_i({\bm{\theta}}_1), \mathcal{T}_i\left(E^{(T)}[{\bm{\theta}}_2])\right)\biggr), \nonumber \\
        \mathcal{L}^t_{stri}&({\bm{\theta}}_2|{\bm{\theta}}_1) = \frac{1}{N_t} \sum_{i=1}^{N_t} \mathcal{L}_{bce}\biggr(\mathcal{T}_i({\bm{\theta}}_2), \mathcal{T}_i\left(E^{(T)}[{\bm{\theta}}_1])\right)\biggr),
        \label{eq:soft_tri}
\end{align}}%
where $\mathcal{T}_i(E^{(T)}[{\bm{\theta}}_1])$ and $\mathcal{T}_i(E^{(T)}[{\bm{\theta}}_2])$ are the soft triplet labels generated by the two networks' past temporally average models. Such soft triplet labels are fixed as training supervisions. By adopting the soft softmax-triplet loss, our MMT framework overcomes the limitation of hard supervisions by the conventional triple loss (\eqref{eq:target_triplet}). It can be successfully trained with soft triplet labels, which are shown to be important for improving the domain adaptation performance in our experiments.
Note that such a softmax-triplet loss was also studied in \citep{zhang2018large}. However, it has never been used to generate soft labels and was not designed to work with soft pseudo labels before.

\begin{algorithm}[t]
\scriptsize
\label{alg}
\begin{algorithmic}
\REQUIRE Target-domain data ${\mathbb{D}}_t$;
\REQUIRE Ensembling momentum $\alpha$ for \eqref{eq:emb}, weighting factors $\lambda^t_{id}$, $\lambda^t_{tri}$ for \eqref{eq:all};
\REQUIRE Initialize pre-trained weights ${\bm{\theta}}_1$ and ${\bm{\theta}}_2$ by optimizing with \eqref{eq:source} on ${\mathbb{D}}_s$. \\
\textbf{for} n in $[1, num\_epochs]$ \textbf{do}\\
\quad Generate hard pseudo labels $\tilde{{\bm{y}}}_i^t$ for each sample ${\bm{x}}_i^t$ in ${\mathbb{D}}_t$ by clustering algorithms. \\
\quad \textbf{for} each mini-batch $B \subset {\mathbb{D}}_t$, iteration $T$ \textbf{do}\\
\quad \quad \textbf{1:} Generate soft pseudo labels from the collaborative networks by predicting
$\mathcal{T}_{i \in B}(E^{(T)}[{\bm{\theta}}_1])$,
$\mathcal{T}_{i \in B}(E^{(T)}[{\bm{\theta}}_2])$,
$C_1^t(F({\bm{x}}^t_{i \in B}|E^{(T)}[{\bm{\theta}}_1]))$,
$C_2^t(F({{\bm{x}}'}^t_{i \in B}|E^{(T)}[{\bm{\theta}}_2]))$; \\
\quad \quad \textbf{2:} Joint update parameters ${\bm{\theta}}_1$ \& ${\bm{\theta}}_2$ by the gradient descent of the objective function \eqref{eq:all}; \\
\quad \quad \textbf{3:} Update temporally average model weights $E^{(T+1)}[{\bm{\theta}}_1]$ \& $E^{(T+1)}[{\bm{\theta}}_2]$ following \eqref{eq:emb}. \\
\quad \textbf{end for}\\
\textbf{end for}
\end{algorithmic}
\caption{\small Unsupervised Mutual Mean-Teaching (MMT) Training Strategy}
\end{algorithm}

\subsubsection{Overall loss and algorithm}
\vspace{-5pt}
Our proposed MMT framework is trained with both off-line refined hard pseudo labels and on-line refined soft pseudo labels.
The overall loss function $\mathcal{L}({\bm{\theta}}_1,{\bm{\theta}}_2)$ simultaneously optimizes the coupled networks, which combines \eqref{eq:target_id},~\eqref{eq:soft_id},~\eqref{eq:hard_tri},~\eqref{eq:soft_tri} and is formulated as,
{\small
\begin{align}
\label{eq:all}
\mathcal{L}({\bm{\theta}}_1,{\bm{\theta}}_2)  &= ~ (1-\lambda^t_{id})( \mathcal{L}^t_{id}({\bm{\theta}}_1)+\mathcal{L}^t_{id}({\bm{\theta}}_2) ) +
\lambda^t_{id}( \mathcal{L}^t_{sid}({\bm{\theta}}_1|{\bm{\theta}}_2) +\mathcal{L}^t_{sid}({\bm{\theta}}_2| {\bm{\theta}}_1))  \nonumber\\
&+ (1-\lambda^t_{tri})(\mathcal{L}^t_{tri}({\bm{\theta}}_1) + \mathcal{L}^t_{tri}({\bm{\theta}}_2))
+ \lambda^t_{tri}( \mathcal{L}^t_{stri}({\bm{\theta}}_1|{\bm{\theta}}_2) +\mathcal{L}^t_{stri}({\bm{\theta}}_2| {\bm{\theta}}_1)),
\end{align}}%
where $\lambda^t_{id}$, $\lambda^t_{tri}$ are the weighting parameters.
The detailed optimization procedures are summarized in Algorithm~\ref{alg}. The hard pseudo labels are off-line refined after training with existing hard pseudo labels for one epoch.
During the training process, the two networks are trained by combining the off-line refined hard pseudo labels and on-line refined soft labels predicted by their peers with proposed soft losses.
The noise and randomness caused by hard clustering, which lead to unstable training and limited final performance, can be alleviated by the proposed MMT framework.

\section{Experiments}
\subsection{Datasets}
\vspace{-5pt}
We evaluate our proposed MMT on three widely-used person re-ID datasets, \textit{i}.\textit{e}.,  Market-1501~\citep{market}, DukeMTMC-reID~\citep{dukemtmc}, and MSMT17~\citep{wei2018person}.
The Market-1501~\citep{market} dataset consists of 32,668 annotated images of 1,501 identities shot from 6 cameras in total, for which 12,936 images of 751 identities are used for training and 19,732 images of 750 identities are in the test set.
DukeMTMC-reID~\citep{dukemtmc} contains 16,522 person images of 702 identities for training, and the remaining images out of another 702 identities for testing, where all images are collected from 8 cameras.
MSMT17~\citep{wei2018person} is the most challenging and large-scale dataset consisting of 126,441 bounding boxes of 4,101 identities taken by 15 cameras, for which 32,621 images of 1,041 identities are spitted for training.
For evaluating the domain adaptation performance of different methods, four domain adaptation tasks are set up, \textit{i}.\textit{e}.,  Duke-to-Market, Market-to-Duke, Duke-to-MSMT and Market-to-MSMT, where only identity labels on the source domain are provided. Mean average precision (mAP) and CMC top-1, top-5, top-10 accuracies are adopted to evaluate the methods' performances.

\subsection{Implementation details}
\vspace{-5pt}

\subsubsection{Training data organization}
\label{sec:data}
\vspace{-5pt}
For both source-domain pre-training and target-domain fine-tuning, each training mini-batch contains 64 person images of 16 actual or pseudo identities (4 for each identity).
Note that the generated hard pseudo labels for the target-domain fine-tuning are updated after each epoch, so the mini-batch of target-domain images needs to be re-organized with updated hard pseudo labels after each epoch.
All images are resized to $256 \times 128$ before being fed into the networks.
\subsubsection{Optimization details}
\label{sec:details}
\vspace{-5pt}
All the hyper-parameters of the proposed MMT framework are chosen based on a validation set of the
Duke-to-Market task with $M_t = 500$ pseudo identities and IBN-ResNet-50 backbone. The same hyper-parameters are then directly applied to the other three domain adaptation tasks.
We propose a two-stage training scheme, where
ADAM optimizer is adopted to optimize the networks with a weight decay of 0.0005.
Randomly erasing~\citep{zhong2017random} is only adopted in target-domain fine-tuning.

\noindent\textbf{Stage 1: Source-domain pre-training.}
We adopt ResNet-50 \citep{he2016deep} or IBN-ResNet-50 \citep{pan2018two} as the backbone networks,
where
IBN-ResNet-50 achieves better performances by integrating both IN and BN modules.
Two same networks are initialized with ImageNet~\citep{deng2009imagenet} pre-trained weights.
Given the mini-batch of images, network parameters ${\bm{\theta}}_1$, ${\bm{\theta}}_2$ are updated independently by optimizing \eqref{eq:source} with $\lambda^s=1$.
The initial learning rate is set to 0.00035 and is decreased to 1/10 of its previous value on the 40th and 70th epoch in the total 80 epochs.

\noindent\textbf{Stage 2: End-to-end training with MMT.}
Based on pre-trained weights ${\bm{\theta}}_1$ and ${\bm{\theta}}_2$,
the two networks are collaboratively updated by optimizing \eqref{eq:all} with the loss weights $\lambda^t_{id}=0.5$, $\lambda^t_{tri}=0.8$.
The temporal ensemble momentum $\alpha$ in \eqref{eq:emb} is set to 0.999.
The learning rate is fixed to 0.00035 for overall 40 training epochs.
We utilize \textit{k-means} clustering algorithm and the number $M_t$ of pseudo classes is set as 500, 700, 900 for Market-1501 and DukeMTMC-reID, and 500, 1000, 1500, 2000 for MSMT17.
Note that actual identity numbers in the target-domain training sets are different from $M_t$. We test different $M_t$ values that are either smaller or greater than actual numbers.

\subsection{Comparison with State-of-the-arts}
\vspace{-5pt}

\begin{table}[tb]
	\scriptsize
	\centering
	\begin{center}
	\begin{tabular}{P{5.3cm}|C{0.6cm}C{0.6cm}C{0.6cm}C{0.7cm}|C{0.6cm}C{0.6cm}C{0.6cm}C{0.7cm}}
	\hline
	\multicolumn{1}{c|}{\multirow{2}{*}{Methods}} & \multicolumn{4}{c|}{Market-to-Duke} & \multicolumn{4}{c}{Duke-to-Market} \\
	\cline{2-9}
	\multicolumn{1}{c|}{} & mAP & top-1 & top-5 & top-10 & mAP & top-1 & top-5 & top-10 \\
	\hline \hline
    PUL~\citep{fan2018unsupervised} (TOMM'18) & 16.4 & 30.0 & 43.4 & 48.5 & 20.5 & 45.5 & 60.7 & 66.7 \\
    TJ-AIDL~\citep{wang2018transferable} (CVPR'18) & 23.0 & 44.3 & 59.6 & 65.0 & 26.5 & 58.2 & 74.8 & 81.1 \\
    SPGAN~\citep{deng2018image} (CVPR'18) & 22.3 & 41.1 & 56.6 & 63.0 & 22.8 & 51.5 & 70.1 & 76.8 \\
    HHL~\citep{zhong2018generalizing} (ECCV'18) & 27.2 & 46.9 & 61.0 & 66.7 & 31.4 & 62.2 & 78.8 & 84.0 \\
    CFSM~\citep{chang2018disjoint} (AAAI'19) & 27.3 & 49.8 &- & -  & 28.3 & 61.2 & - & - \\
    BUC~\citep{lin2019aBottom} (AAAI'19) & 27.5 & 47.4 & 62.6 & 68.4 & 38.3 & 66.2 & 79.6 & 84.5 \\
    ARN~\citep{li2018adaptation} (CVPR'18-WS) & 33.4 & 60.2 & 73.9 & 79.5 & 39.4 & 70.3 & 80.4 & 86.3 \\
    UDAP~\citep{song2018unsupervised} (Arxiv'18) & 49.0 & 68.4 & 80.1 & 83.5 & 53.7 & 75.8 & 89.5 & 93.2 \\
    ENC~\citep{zhong2019invariance} (CVPR'19) & 40.4 & 63.3 & 75.8 & 80.4 & 43.0 & 75.1 & 87.6 & 91.6 \\
    UCDA-CCE~\citep{qi2019novel} (ICCV'19) & 31.0 & 47.7 & - & - & 30.9 & 60.4 & - & - \\
    PDA-Net~\citep{li2019cross} (ICCV'19) & 45.1 & 63.2 & 77.0 & 82.5 & 47.6 & 75.2 & 86.3 & 90.2 \\
    PCB-PAST~\citep{zhang2019self} (ICCV'19) & 54.3 & 72.4 & - & - & 54.6 & 78.4 & - & - \\
    SSG~\citep{yang2019selfsimilarity} (ICCV'19) & 53.4 & 73.0 & 80.6 & 83.2 & 58.3 & 80.0 & 90.0 & 92.4 \\
    \hline
	{Co-teaching \citep{han2018co}-500 (ResNet-50)} & 55.7 & 71.9 & 83.5 & 88.1 & 65.1 & 82.5 & 91.8 & 93.4 \\
	{Co-teaching \citep{han2018co}-500 (IBN-ResNet-50)} & 61.7 & 77.6 & 88.0 & 90.7 & 71.7 & 87.8 & 95.0 & 96.5 \\
    \hline
    Pre-trained (ResNet-50) & 29.6 & 46.0 & 61.5 & 67.2  & 31.8 & 61.9 & 76.4 & 82.2 \\
	Proposed MMT-500 (ResNet-50) & {63.1}	& {76.8} & 88.0 & 92.2  & \textbf{71.2} & \textbf{87.7} & \textbf{94.9} & \textbf{96.9} \\
	Proposed MMT-700 (ResNet-50) & \textbf{65.1}  & \textbf{78.0} & \textbf{88.8} & \textbf{92.5} & 69.0 & 86.8 & 94.6 & \textbf{96.9}  \\
	Proposed MMT-900 (ResNet-50) & 63.1 & 77.4 & 88.1 & \textbf{92.5} &	66.2 & 86.8 & \textbf{94.9} & 96.6 \\
	\hline
    Pre-trained (IBN-ResNet-50)& 35.4 & 54.0 & 67.7 & 72.9  & 35.6 & 65.3 & 79.7 & 84.3 \\
	Proposed MMT-500 (IBN-ResNet-50)& {65.7} & {79.3} & 89.1 & 92.4  & 	\textbf{76.5} & 90.9 & 96.4 & 97.9 \\
	Proposed MMT-700 (IBN-ResNet-50) & \textbf{68.7}& \textbf{81.8}& \textbf{91.2}& \textbf{93.4}&	74.5 & 91.1  & \textbf{96.5} & \textbf{98.2} \\
	Proposed MMT-900 (IBN-ResNet-50) & 67.3&80.8& 90.3 & 93.0 &	72.7 & \textbf{91.2} & 96.3 & 98.0 \\
	\hline
	\end{tabular}
\vspace{1pt}
	\begin{tabular}{P{5.3cm}|C{0.6cm}C{0.6cm}C{0.6cm}C{0.7cm}|C{0.6cm}C{0.6cm}C{0.6cm}C{0.7cm}}
	\hline
	\multicolumn{1}{c|}{\multirow{2}{*}{Methods}} & \multicolumn{4}{c|}{Market-to-MSMT} & \multicolumn{4}{c}{Duke-to-MSMT} \\
	\cline{2-9}
	\multicolumn{1}{c|}{} & mAP & top-1 & top-5 & top-10 & mAP & top-1 & top-5 & top-10 \\
	\hline \hline
    PTGAN~\citep{wei2018person} (CVPR'18) & 2.9 & 10.2 & - & 24.4 & 3.3 & 11.8 & - & 27.4 \\
    ENC~\citep{zhong2019invariance} (CVPR'19) & 8.5 & 25.3 & 36.3 & 42.1 & 10.2 & 30.2 & 41.5 & 46.8 \\
    SSG~\citep{yang2019selfsimilarity} (ICCV'19) & 13.2 & 31.6 &- & 49.6 & 13.3 & 32.2 & - & 51.2 \\
    \hline
    Pre-trained (ResNet-50)  & 7.1 & 19.4 & 28.9 & 34.2 & 9.4 & 27.0 & 38.1 & 43.7 \\
	Proposed MMT-500 (ResNet-50)  & 16.6 & 37.5 & 50.6 & 56.5 & 17.9 & 41.3	& 54.2 & 59.7 \\
	Proposed MMT-1000 (ResNet-50)  & {21.6} & {46.1} & {59.8} & {66.1} & \textbf{23.5} & {50.0} & {63.6} & {69.2}	 \\
	{Proposed MMT-1500 (ResNet-50)} & \textbf{22.9} & \textbf{49.2} & \textbf{63.1} & \textbf{68.8} & 23.3 & \textbf{50.1} & \textbf{63.9} & \textbf{69.8} \\
	{Proposed MMT-2000 (ResNet-50)} & 20.8 & 45.7 & 59.6 & 65.6 & 22.4 & 49.0 & 62.5 & 67.8  \\
	\hline
    Pre-trained (IBN-ResNet-50)  & 9.5  & 25.3 & 36.2 & 41.6 & 11.9 & 32.6 & 44.7 & 50.4 \\
	Proposed MMT-500 (IBN-ResNet-50) & 19.6 & 43.3 & 56.1 & 61.6  & 23.3 & 50.0 & 62.8 & 68.4  \\
	Proposed MMT-1000 (IBN-ResNet-50) & {26.3} & {52.5} & {66.3} & {71.7} & \textbf{29.7} & \textbf{58.8} & {71.0} & {76.1} \\
	{Proposed MMT-1500 (IBN-ResNet-50)} & \textbf{26.6} & \textbf{54.4} & \textbf{67.6} & \textbf{72.9} & 29.3 & 58.2 & \textbf{71.6} & \textbf{76.8} \\
	{Proposed MMT-2000 (IBN-ResNet-50)} & 25.1 & 52.7 & 65.9 & 71.3 & 28.1 & 56.8 & 70.8 & 76.0 \\
	\hline
	\end{tabular}

	\end{center}
	\vspace{-10pt}
	\caption{
Experimental results of the proposed MMT and state-of-the-art methods on Market-1501~\citep{market}, DukeMTMC-reID~\citep{dukemtmc}, and MSMT17~\citep{wei2018person} datasets, where MMT-$M_t$ represents the result with $M_t$ pseudo classes. Note that none of $M_t$ values equals the actual number of identities but our method still outperforms all state-of-the-arts.}
	\label{tab:sota}
\end{table}

We compare our proposed MMT framework with state-of-the-art methods
on the four domain adaptation tasks, Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT. The results are shown in Table~\ref{tab:sota}.
Our MMT framework significantly outperforms all existing approaches with both ResNet-50 and IBN-ResNet-50 backbones,
which verifies the effectiveness of our method.
Moreover, we almost approach fully-supervised learning performances \citep{sun2018beyond,ge2018fd} without any manual annotations on the target domain.
No post-processing technique, \textit{e}.\textit{g}.  re-ranking~\citep{zhong2017re} or multi-query fusion~\citep{market}, is adopted.

Specifically,
by adopting the ResNet-50~\citep{he2016deep} backbone,
we surpass the state-of-the-art clustering-based SSG~\citep{yang2019selfsimilarity} by considerable margins of 11.7\% and 12.9\% mAP on Market-to-Duke and Duke-to-Market tasks with simpler network architectures and lower output feature dimensions.
Furthermore, evident 9.7\% and 10.2\% mAP gains are achieved on Market-to-MSMT and Duke-to-MSMT tasks. Recall that $M_t$ is the number of clusters or number of hard pseudo labels manually specified.
More importantly,
we achieve state-of-the-art performances on all tested target datasets with different $M_t$, which are either fewer or more than the actual number of identities in the training set of the target domain.
Such results prove the necessity and effectiveness of our proposed pseudo label refinery for hard pseudo labels with inevitable noises.

To compare with relevant methods for tackling general noisy label problems,
we implement Co-teaching \citep{han2018co} on unsupervised person re-ID task with 500 pseudo identities on the target domain, where the noisy labels are generated by the same clustering algorithm as our MMT framework.
The hard classification (cross-entropy) loss is adopted on selected clean batches.
All the hyper-parameters are set as the same for fair comparison, and the experimental results are denoted as ``Co-teaching \citep{han2018co}-500'' with both ResNet-50 and IBN-ResNet-50 backbones in Table \ref{tab:sota}.
Comparing ``Co-teaching \citep{han2018co}-500 (ResNet-50)'' with ``Proposed MMT-500 (ResNet-50)'', we observe significant 7.4\% and 6.1\% mAP drops on Market-to-Duke and Duke-to-Market tasks respectively,
since Co-teaching \citep{han2018co} is designed for general close-set recognition problems with manually generated label noise, which could not tackle the real-world challenges in unsupervised person re-ID.
More importantly, it does not explore how to mitigate the label noise for the triplet loss as our method does.

\subsection{Ablation Studies}
\label{sec:abla}
\vspace{-5pt}

\begin{table}[tb]

	\scriptsize
	\centering
	\begin{center}
	\begin{tabular}{P{4.5cm}|C{0.7cm}C{0.7cm}C{0.7cm}C{0.7cm}|C{0.7cm}C{0.7cm}C{0.7cm}C{0.7cm}}
	\hline
	\multicolumn{1}{c|}{\multirow{2}{*}{Duke-to-Market}} & \multicolumn{4}{c|}{IBN-ResNet-50} & \multicolumn{4}{c}{ResNet-50} \\
	\cline{2-9}
	\multicolumn{1}{c|}{} & mAP & top-1 & top-5 & top-10 & mAP & top-1 & top-5 & top-10 \\
	\hline \hline
	Pre-trained (only $\mathcal{L}^s_{id}$ \& $\mathcal{L}^s_{tri}$) & 35.6 & 65.3 & 79.7 & 84.3 & 31.8 & 61.9 & 76.4 & 82.2  \\
	Baseline (only $\mathcal{L}^t_{id}$ \& $\mathcal{L}^t_{tri}$) & 62.7 & 84.4 & 92.7 & 95.5 & 53.5 & 76.0 & 88.1 & 91.9 \\
	\hline
	{Baseline+MMT-500 (only $\mathcal{L}^t_{sid}$ \& $\mathcal{L}^t_{stri}$)} & 34.5 & 59.7 & 73.0 & 78.0 & 22.4 & 46.5 & 61.5 & 67.4 \\
	{Baseline+MMT-500 (w/o $\mathcal{L}^t_{id}$)} & 38.0 & 63.4 & 74.9 & 79.4 & 24.9 & 50.3 & 64.0 & 69.8 \\
	{Baseline+MMT-500 (w/o $\mathcal{L}^t_{tri}$)} & 76.2 & 90.8 & \textbf{96.6} & \textbf{97.9} & \textbf{72.0} & \textbf{87.8} & \textbf{95.5} & \textbf{96.9} \\
	Baseline+MMT-500 (w/o $\mathcal{L}^t_{sid}$) & 69.6 & 87.4 & 95.2 & 96.7 & 62.6 & 84.0 & 93.4 & 95.4 \\
	Baseline+MMT-500 (w/o $\mathcal{L}^t_{stri}$) & 71.7 & 88.5 & 95.1 & 96.6 & 65.9 & 84.0 & 93.1 & 95.5 \\
	\hline
	Baseline+MMT-500 (w/o ${\bm{\theta}}_2$) & 72.8 & 89.1 & 95.2 & 97.1 & 67.5 & 86.1 & 94.3 & 96.1 \\
	Baseline+MMT-500 (w/o $E[{\bm{\theta}}]$) & 72.1 & 88.7 & 95.4 & 97.3 & 62.3 & 80.5 & 91.3 & 94.0 \\
	\hline
	Baseline+MMT-500 & \textbf{76.5} & \textbf{90.9} & {96.4} & \textbf{97.9} & {71.2} & {87.7} & {94.9} & \textbf{96.9} \\
	\hline
	\end{tabular}
\vspace{1pt}
	\begin{tabular}{P{4.5cm}|C{0.7cm}C{0.7cm}C{0.7cm}C{0.7cm}|C{0.7cm}C{0.7cm}C{0.7cm}C{0.7cm}}
	\hline
	\multicolumn{1}{c|}{\multirow{2}{*}{Market-to-Duke}} & \multicolumn{4}{c|}{IBN-ResNet-50} & \multicolumn{4}{c}{ResNet-50} \\
	\cline{2-9}
	\multicolumn{1}{c|}{} & mAP & top-1 & top-5 & top-10 & mAP & top-1 & top-5 & top-10 \\
	\hline \hline
	Pre-trained (only $\mathcal{L}^s_{id}$ \& $\mathcal{L}^s_{tri}$) & 35.4 & 54.0 & 67.7 & 72.9 & 29.6 & 46.0 & 61.5 & 67.2 \\
	Baseline (only $\mathcal{L}^t_{id}$ \& $\mathcal{L}^t_{tri}$) & 55.0 & 72.3 & 84.4 & 88.1 & 48.2 & 66.4 & 79.8 & 84.0 \\
	\hline
	{Baseline+MMT-500 (only $\mathcal{L}^t_{sid}$ \& $\mathcal{L}^t_{stri}$)} & 24.5 & 38.0 & 50.1 & 56.1 & 13.6 & 24.3 & 36.4 & 42.5 \\
	{Baseline+MMT-500 (w/o $\mathcal{L}^t_{id}$)} & 27.5 & 42.0 & 53.9 & 60.3 & 15.3 & 25.8 & 37.7 & 43.7  \\
	{Baseline+MMT-500 (w/o $\mathcal{L}^t_{tri}$)} & 65.6 & \textbf{79.4} & \textbf{89.8} & 92.3 & 63.0 & \textbf{77.3} & \textbf{88.3} & 91.6 \\
	Baseline+MMT-500 (w/o $\mathcal{L}^t_{sid}$) & 60.3 & 75.7 & 86.6 & 89.9 & 58.1 & 74.9 & 85.2 & 89.5  \\
	Baseline+MMT-500 (w/o $\mathcal{L}^t_{stri}$) & 61.7 & 77.1 & 86.5 & 89.6  & 59.5 & 73.9 & 85.5 & 88.8  \\
	\hline
	Baseline+MMT-500 (w/o ${\bm{\theta}}_2$) & 62.1 & 77.6 & 86.8 & 89.7 & 58.2 & 74.1 & 86.0 & 89.3 \\
	Baseline+MMT-500 (w/o $E[{\bm{\theta}}]$) & 61.1 & 76.3 & 86.6 & 89.8 & 55.7 & 70.0 & 83.6 & 87.2 \\
	\hline
	Baseline+MMT-500 & \textbf{65.7} & {79.3} & {89.1} & \textbf{92.4} & \textbf{63.1} & {76.8} & {88.0} & \textbf{92.2} \\
	\hline
	\end{tabular}

	\end{center}
	\vspace{-10pt}
	\caption{Ablation studies of our proposed MMT on Duke-to-Market and Market-to-Duke tasks with $M_t$ of 500. Note that the actual numbers of identities are not equal to 500 for both datasets but our MMT method still shows significant improvements.}

	\label{tab:ablation}
\end{table}

In this section, we evaluate each component in our proposed framework by conducting ablation studies on Duke-to-Market and Market-to-Duke tasks with both ResNet-50~\citep{he2016deep} and IBN-ResNet-50~\citep{pan2018two} backbones. Results are shown in Table~\ref{tab:ablation}.

\noindent\textbf{Effectiveness of the soft pseudo label refinery.}
To investigate the necessity of handling noisy pseudo labels in clustering-based UDA methods,
we create baseline models that utilize only off-line refined hard pseudo labels, \textit{i}.\textit{e}.,  optimizing \eqref{eq:all} with $\lambda^t_{id}=\lambda^t_{tri}=0$ for the two-step training strategy in Section~\ref{sec:revisit}.
The baseline model performances are present in Table~\ref{tab:ablation} as ``Baseline (only $\mathcal{L}^t_{id}$ \& $\mathcal{L}^t_{tri}$)''.
Considerable drops of 17.7\% and 14.9\% mAP are observed on ResNet-50 for Duke-to-Market and Market-to-Duke tasks.
Similarly, 13.8\% and 10.7\% mAP decreases are shown on the IBN-ResNet-50 backbone.
Stable increases achieved by the proposed on-line refined soft pseudo labels on different datasets and backbones demonstrate the necessity of soft pseudo label refinery and the effectiveness of our proposed MMT framework.

\noindent\textbf{Effectiveness of the soft softmax-triplet loss.}
We also verify the effectiveness of soft softmax-triplet loss with softly refined triplet labels in our proposed MMT framework.
Experiments of removing the soft softmax-triplet loss, \textit{i}.\textit{e}.,  $\lambda^t_{tri}=0$ in \eqref{eq:all}, but keeping the hard softmax-triplet loss (\eqref{eq:hard_tri}) are conducted, which are denoted as ``Baseline+MMT-500 (w/o $\mathcal{L}^t_{stri}$)''.
All experiments without the supervision of soft triplet loss show distinct drops on Duke-to-Market and Market-to-Duke tasks, which indicate that the hard pseudo label with hard triplet loss hinders the feature learning capability because it ignores pseudo label noise by the clustering algorithms.
Specifically,
the mAP drops are 5.3\% on ResNet-50 and 4.8\% on IBN-ResNet-50 when evaluating on the target dataset Market-1501.
As for the Market-to-Duke task, similar mAP drops of 3.6\% and 4.0\% on the two network structures can be observed.
An evident improvement of up to 5.3\% mAP demonstrates the usefulness of our proposed soft softmax-triplet loss.

\noindent\textbf{Effectiveness of Mutual Mean-Teaching.}
We propose to generate on-line refined soft pseudo labels for one network with the predictions of the past average model of the other network in our MMT framework, \textit{i}.\textit{e}.,  the soft labels for network $1$ are output from the average model of network $2$ and vice versa.
{We observe that the soft labels generated in such manner are more reliable due to the better decoupling between the past temporally average models of the two networks.}
Such a framework could effectively avoid bias amplification even when the networks have much erroneous outputs in the early training epochs.
There are two possible simplification our MMT framework {with less de-coupled structures}. The first one is to keep only one network in our framework and use its past temporal average model to generate soft pseudo labels for training itself. Such experiments are denoted as ``Baseline+MMT-500 (w/o ${\bm{\theta}}_2$)''.
The second simplification is to na\"ively use one network's current-iteration predictions as the soft pseudo labels for training the other network and vice versa, \textit{i}.\textit{e}.,  $\alpha=0$ for \eqref{eq:emb}. This set of experiments are denoted as ``Baseline+MMT-500 (w/o $E[{\bm{\theta}}]$)''.
Significant mAP drops compared to our proposed MMT could be observed in the two sets of experiments, especially when using the ResNet-50 backbone, \textit{e}.\textit{g}.  the mAP drops by 8.9\% on Duke-to-Market task when removing past average models.
This validates the necessity of employing the proposed mutual mean-teaching scheme for providing more robust soft pseudo labels.
In despite of the large margin of performance declines when removing either the peer network or the past average model,
our proposed MMT outperforms the baseline model significantly,
which further demonstrates the importance of adopting the proposed on-line refined soft pseudo labels.

\noindent\textbf{Necessity of hard pseudo labels in proposed MMT.}
Despite the robust soft pseudo labels bring significant improvements,
the noisy hard pseudo labels are still essential to our proposed framework,
since the hard classification loss $\mathcal{L}^t_{id}$ is the foundation for capturing the target-domain data distributions.
To investigate the contribution of $\mathcal{L}^t_{id}$ in the final training objective function as \eqref{eq:all},
we conduct two experiments. (1) ``Baseline+MMT-500 (only $\mathcal{L}^t_{sid}$ \& $\mathcal{L}^t_{stri}$)'' by removing both hard classification loss and hard triplet loss with $\lambda^t_{id}=\lambda^t_{tri}=1$; (2)``Baseline+MMT-500 (w/o $\mathcal{L}^t_{id}$)'' by removing only hard classification loss with $\lambda^t_{id}=1$.
As illustrated in Table \ref{tab:ablation},
the above two experiments both result in much lower performances than the model pre-trained on the source domain (``Pre-trained (only $\mathcal{L}^s_{id}$ \& $\mathcal{L}^s_{tri}$)''), which effectively validate the necessity of $\mathcal{L}^t_{id}$.
The initial network usually outputs uniform probabilities for each identity, which act as soft labels for soft classification loss, since it could not correctly distinguish between different identities on the target domain.
Directly training with such smooth and noisy soft pseudo labels, the networks in our framework would soon collapse due to the large bias.
One-hot hard labels for classification loss are critical for learning discriminative representations on the target domain.
In contrast,
the hard triplet loss $\mathcal{L}^t_{tri}$ is not absolutely necessary in our framework,
as experiments without $\mathcal{L}^t_{tri}$, denoted as ``Baseline+MMT-500 (w/o $\mathcal{L}^t_{tri}$)'' with $\lambda^t_{tri}=1.0$, show similar performances as our final results with $\lambda^t_{tri}=0.8$.
It is much easier to learn to predict robust soft labels for the soft softmax-triplet loss in \eqref{eq:soft_tri}  even at early training epochs,
which has only two classes, \textit{i}.\textit{e}.,  positive and negative.

\section{Conclusion}
\vspace{-5pt}
In this work, we propose an unsupervised Mutual Mean-Teaching (MMT) framework to tackle the problem of noisy pseudo labels in clustering-based unsupervised domain adaptation methods for person re-ID.
The key is to conduct pseudo label refinery to better model inter-sample relations in the target domain by optimizing with the off-line refined hard pseudo labels and on-line refined soft pseudo labels in a collaborative training manner.
Moreover, a novel soft softmax-triplet loss is proposed to support learning with softly refined triplet labels for optimal performances.
Our method significantly outperforms all existing person re-ID methods on domain adaptation task with up to 18.2\% improvements.

\subsubsection*{Acknowledgments}
This work is supported by the General Research Fund sponsored by the Research Grants Council of Hong Kong (\textit{Nos.} CUHK14208417, CUHK14239816, CUHK14207319), the Hong Kong Innovation and Technology Support Program (\textit{No.} ITS/312/18FX).

redpajama_set_name: RedPajamaArXiv


text:
Media Landscapes Expert Analyses of the State of Media
Television and social networks are the most popular media distribution platforms among Ukrainians, almost 70 percent of the population uses them as a main source of information.
Press freedom ranking
Internet penetration
Peace index ranking
Most trusted medium
Net trust index
Semi-presidential Republic
Government type
Journalists associations
Audience measurement organisations
Media legislation
Universities and schools
Media development organisations
Mobile network ecosystem
Main trends
Mobile ownership
Landscape analysis
Profiles of main tech parks, accelerators, hackathons
Traditional forms of communication
Andrii Ianitskyi
News agencies are very important in the daily functioning of the media system. Many media often rely on what news agencies put out for their content. This is especially the case for regional media, news websites, and radio stations. The problem is that the information provided by news agencies is rarely verified by the newsrooms. If mistakes or distortions appear in the initial messages put out by news agencies, they are quickly spread around the readers all over Ukraine. Even when the news agencies correct the mistakes, many media do not.
However, Ukrainian news agencies, like most other media, are scarcely independent as they are often part of big media groups owned by oligarchs. For instance, UNIAN news agency is included in 1+1 Media Group owned by Ihor Kolomoisky. Ukrainski Novyny (Ukrainian News) is a part of Inter Media Group which belongs to Dmytro Firtash and Serhiy Liovochkin. Although Interfax-Ukraine declares full independence, it operates under a license from the Russian agency Interfax. Ukrinform is a state-owned agency that is subject to the influence of the Ukrainian government. Ukraine also has offices for international news agencies such as Bloomberg, Reuters, or AFP.
< Journalists associations
Audience measurement organisations >
We're looking to expand our roster of paid writers.
If your country is not yet featured on Media Landscapes, contact us.
If you are interested in sponsoring a particular country report,
or this project as a whole, please get in touch.
Copyright © 1992 - 2021 - European Journalism Centre. All rights reserved. Privacy policy and Terms of service.

redpajama_set_name: RedPajamaCommonCrawl


text:
Azerbaijan may export snake venom
By Naila Huseynli
Caucasus most widely spread species of poisonous viper may bring economic benefits to Azerbaijan. The country, which once produced the largest amount of snake venom of Levantine viper in the Soviet Union, can redevelop venom production capacity.
Azerbaijan National Academy of Sciences (ANAS) has started negotiations on sale of snake venom.
In this regard, Elman Yusifov, Director of the Institute of Zoology of ANAS stated that currently negotiations on the export of the viper poison inherited from Soviet era continue with the Turkey's Marmara Trade.
"At present, we are at the stage of correspondence. They applied for the purchase of a part of the Soviet-era poison. Part of that poison will be tested by them in an international laboratory. After receiving accurate information about the suitability of the poison the second stage will start," Yusifov said.
He also added that about 20 kg of snake venom have been inherited during the Soviet era, adding that the Marmara Trade intends to buy 8-10 kg of this poison.
Yusifov said that the price of 1 gram of snake poison varies from $100 to $500 in the world market.
"It should be considered that poison in Azerbaijan is not a new product. Although Soviet-era poison is preserved in optimal condition, in vacuum ampoules, toxicity and other indicators may change in their composition. This may have a certain impact on the purchase price of the poison," he explained.
The director of the institute also stated that if the export proceeds as a result of the negotiations with the Turkish company, then Institute of Zoology will raise the issue of restoration of production of snake venom in the Department of Biological and Medical Sciences and Presidium of ANAS.
The snake poison was produced in Azerbaijan in the Soviet era and the strongest production center was in the country, Yusifov reminded. During that time, 5-10 kilograms of poison were produced and sold abroad.
After the collapse of Soviet Union, relations were interrupted with the buyers of Azerbaijan due to new cooperation with Iran, Afghanistan and other countries.
Snake venom could be a remedy to many diseases, particularly neuralgia, arthralgia, myalgia, myositis, rheumatism, hypertension, and asthma. Snake venom in very small quantities is frequently used for medical purposes as an analgesic and an anti-inflammatory remedy.


text:
title: "Koding + GitLab: Contribute code faster"
author: Sid Sijbrandij
author_twitter: sytses
categories: integration
image_title: '/images/blogimages/koding-and-gitlab-integrated-cover.png'
twitter_image: '/images/tweets/koding-and-gitlab-integrated.png'
description: "Koding and GitLab are integrated to make contributing code faster!"
---

Today, developers spend hours creating dev environments before they can write a single line of code.
We think that's backwards. Development is collaborative and most developers work across a variety of environments
so the time spent building unique dev environments builds up pretty quickly. With [Koding] and GitLab, developers
never have to waste time on this again.

<!-- more -->

## Our vision

Our vision is that developers should be able to contribute code faster with one-click to build
every project, issue, and merge request in a complete IDE. Koding makes it so much easier to get started.
If you see an issue you want to work on, it only takes one-click to build an environment where you
and your team can work together. You don't have to configure anything locally, you can just start coding.
With Koding and GitLab, teams will be able to work together seamlessly by having every GitLab repo buildable
on any cloud. You can get started as a contributor, check out merge requests, and inspect failing tests faster
and easier than ever. Our hope is that removing the burden of building dev environments will help developers
collaborate on more projects and focus on writing code that will improve their products.

<figure class="video_container">
  <iframe src="https://www.youtube.com/embed/7rX_KzCMNtk" frameborder="0" allowfullscreen="true"> </iframe>
</figure>

## Faster from idea to production

At GitLab, we want to enable teams to go faster from idea to production by using one integrated set of tools for
the software development lifecycle. We see a world where you chat about something, turn that idea into an issue
without leaving the chat interface, spin up an IDE to work on the issue,  commit your changes, run tests with GitLab
CI, let peers review and edit your code, and once it's ready you can deploy to the necessary environment. Then after
you've deployed to production, you'll be able to review how your team progressed from idea to production. This final
step is important feedback to help you improve the development process by understanding the overall health of your
project as well as the time spent in each stage. We are working on an updated video to show you all of the steps I've
just shared. In the meantime, watch our [current demo](https://youtu.be/pY4IbEXxxGY) and tell us what you think.

## What's next

Both GitLab and [Koding] are excited about what we have coming with our integration. We hope to deliver the first
iteration in our next release on August 22nd. If you're interested in learning more or you want to share your ideas,
check out [this issue](https://gitlab.com/gitlab-org/gitlab-ce/merge_requests/4769) that details more of our plans.

[koding]: http://www.koding.com/

redpajama_set_name: RedPajamaGithub


text:
Wolfeboro In Exile »
Wolfeboro Politics »
old coot - Thu, 06/25/2009 - 10:59am
I was glad to see that the Wolfeboro BOS FINALLY dropped their rediculous lawsuit against the Wiggins of North Wolfeboro. Finally, a defeat for North Wolfeboro's "Wooden Shutter Mafia".
Wolfeboro Politics
Grunter article about suit against the ZBA
wolf - Thu, 06/25/2009 - 11:52am
Below is the article that was in the Grunter about the ending of the suit:
BY THOMAS BEELER
WOLFEBORO — Following a lengthy nonpublic session last Wednesday evening, June 3, Board of Selectmen Chair Dave Senecal announced that the majority of selectmen had voted to end the town's appeal of a March 2008 decision by the Zoning Board of Adjustment
(ZBA) granting a building permit for a house on a Class VI road. Senecal added that board is instructing town counsel to withdraw the suit in Carroll County Superior Court. When asked by resident Suzanne Ryan who voted for ending the suit, Senecal replied "the majority,"
which he then clarified as three out of five. Given the facts that Selectmen Linda Murray and Sarah Silk are the only two selectmen remaining
from the board that denied the building permit to Mark and Carol Wiggins and that Senecal and Selectman Kristi Ginter are on record as favoring the permit, the third vote must have come from Selectman Marge Webster, who was elected last March. The previous Monday
evening, June 1, a three member panel of the ZBA had voted to respond to Carroll County Superior Court Judge Steven M. Houran's
request to provide in writing the reasons why the ZBA felt that the Wiggins' request had met the four tests for granting a building permit on a Class VI road required by RSA 674:41,II.ZBA counsel Laura Spector was asked to prepare a draft response, based on the written comments of the three members, for review by the ZBA at its next meeting on July 6. Reached on Thursday, June 4, Spector said the selectmen's decision to withdraw the suit would make the ZBA response moot because the case would not go any further. Spector also confirmed that the ZBA had met with counsel the week prior to the Monday meeting and did discuss the possibility that selectmen might withdraw the suit.
Mark Wiggins said he was "thrilled" by the decision and "very grateful" for the support he has received from many people in town who have written to selectmen and shown up at meetings to urge that the building permit be granted. Opposition In the Public Comment section of the June 3 meeting, just before selectmen went into nonpublic session to discuss the Wiggins permit appeal suit, Ken Perry, President of the North Wolfeborough Area Association (NWAA) read a statement to selectmen that addressed newspaper reports of statements made during
public input at selectmen's meetings about granting the Wiggins' a building permit on the Class VI portion of Trask Mountain Road. Perry pointed out that while the 2006 Master Plan was not adopted until after the Wiggins applied for a building permit, "the MP 'clock' started when advertised Public Hearings on the 2006 MP were held in Nov & Dec of 2006, months before Mark Wiggins' initial application."
The 2006 Master Plan states that building permits should not be issued on Class VI roads. The building permit application was filed in March 2007. Perry's second point was that Wiggins paid only $1,600 an acre for the 67-acre lot in 2003 "when similarsized Wolfeboro parcels, of
lesser quality but on Class V roads, were selling for 2 & 3 times as much. He advertised his land for sale in 2006 for $330K to $199K. Unable to sell, he then asked the town to Add Value to his questionable investment. This all occurred before the NWAA made any public
statements on the issue." Perry added that selectmen "did not consider only what NWAA members supported" in deciding to appeal the ZBA decision to grant the permit to Carroll County Superior Court. The other factors included future Municipal Electric and emergency service costs and the results of the 2006 Community Survey, where more than 83 percent rated preserving open space, historic neighborhoods and
Wolfeboro's rural character as "very important" or "somewhat important." Finally he provided information on Class VI roads in town. There are 16 Class VI roads, of which "2/3 are within or border the Agricultural District (northern ? of the town)" and "7 are in one or both of the Historic Districts." "Will one home change our neighborhood? Absolutely not. However, what about the 2nd, 3rd, 4th, etc.home on the Class VI road? Is it possible that nearby properties will lose some value if Trask Mountain Road is eventually open to through traffic? Absolutely."
"I respectfully ask the Board of Selectmen to continue to allow the Class VI road issue to be processed by the ZBA." Selectmen listened attentively to Perry's remarks, but in the end reached the decision to withdraw the suit by a 3 to 2 margin.
We Doan Need No Steenkin' Rules!
Peace_through_W... - Thu, 06/25/2009 - 12:52pm
Rules are meant to be broken, I guess.
Thanks wolf...
old coot - Thu, 06/25/2009 - 12:57pm
for posting the relevant article.
I agree too. I really can't
tis - Thu, 06/25/2009 - 6:44pm
I agree too. I really can't figure the whole thing out, it seems they singled them out.
old coot - Thu, 06/25/2009 - 8:50pm
The Town of Wolfeboro didn't need to keep spending our money on an attorney to keep kicking the same dead horse. That's why the case was dropped IMHO.
"Thanks Wolf, for posting the relevant article...."
Peace_through_W... - Fri, 06/26/2009 - 7:06am
You read that the Town of Alton has demanded decades of documentation from that huge "Ames Farm" boat-launch site?
That Town has its fingers out for more tax money: I'd expect a hundred Condo units to go in there.
Alton has already blocked Ames Farm from continuing their boat launch. (To pay Alton property taxes—Just like they put Alton's much-enjoyed summer camp business out of business for multi-millionaire Bob Bahre).
This is even worse than the IRS going back the 10-years (they are permitted to) to examine your tax records.
Wolfeboro seems to be keying-in on the very same track.
Isn't the Ames Farm in
tis - Fri, 06/26/2009 - 8:13am
Isn't the Ames Farm in Gilford, Peace? I think it is but I could be wrong.
tis Scores!
old coot - Fri, 06/26/2009 - 1:47pm
You're right tis! And probably the Ames Farm owners do wish that their property was located in Alton, seeing as how the town they are in (which you correctly identified as Gilford] just won its lawsuit against the Ames Farm boat ramp operation, which, while dating back eons, didn't qualify as grandfathered.
Seeing as how the Ames Farm's owners have paid taxes to Gilford for more than a century and the ramp had been there for decades, I think that Gilford abused the power that it exercised.
I agree with you oc. I think
tis - Fri, 06/26/2009 - 6:19pm
I agree with you oc. I think they should be allowed to continue. How much impact does it make?
Isn't the Ames Farm in Gilford, Peace?
Peace_through_W... - Fri, 06/26/2009 - 9:06pm
I wasn't certain, but I think you're right.
I meant to include Alton in a money-grab—in place of a large summer camp bringing in fewer tax dollars every year. IOW, I would have voted in favor of keeping a summer lakefront for kids and written off any "lost-revenues".
It was immoral what they did.
"...Seeing as how the Ames Farm's owners have paid taxes to Gilford for more than a century and the ramp had been there for decades, I think that Gilford abused the power that it exercised..."
P_T_Weakness asks:
"Ya think?"
old coot says, "You betcha!"
Mutiny's Afoot
It's good to see that the Historic District Monarchy is crumbling from the forces of their own historic arrogance.
A couple of changes


Freshman Mackenzie Womack is a reporter for the Courier. She is most excited about the sports articles and other interesting topics to write about. Outside of school, she enjoys hanging out with friends and playing a lot of basketball. While involved with the Courier, she hopes to gain knowledge in photography and become a better writer.


"This app brings a new level of fun to the course"
"Perfect for any light-hearted round of golf"
1) Open Shank! & act like your taking a picture.
2) Wait for them to swing & tap the goat.
3) The goat SCREAMS and causes a Shank!
4) You catch it all on camera!
Whether you're out for a round with some old buddies, your kids, or someone new, Shank! is guaranteed to deliver a good laugh and even better videos for you to share with everyone.
A great way to make golf with your friends a little more entertaining!
I've had so much fun using this app! There's nothing better than playing a joke on your golf partner and making them shank their golf swing and capturing their reaction with the video feature on this app. Their reaction is priceless and always ends in a good laugh with my golf buddies.
Such a great idea! I love being able to pull this app open and mess with my friends when golfing. It never gets old!
They could definitely add more features but this is pretty funny for a first version.


text:
Parceiros e regiões
Parcerias globais
Nossa estratégia
Modelo de parceria
Os problemas
Publicações e recursos
Motivos famintos para refletir e agir
Livreto sobre Soberania alimentar
Terra e fome: Como criar as conexões corretas
Workshop sobre a Água
Aliança de Justiça Global da Grassroots (GGJ) / Marcha Mundial das Mulheres (WMW-US)
Grupo de Trabalho em Defesa do Haiti (HAWG)
Campanha dos EUA pelos direitos palestinos
Aliança de Soberania Alimentar dos EUA
Nosso impacto
Histórias de doadores
Doadora em destaque: Deborah Milkowski
Doador em destaque: Jim Graham
Doador em destaque: Clark Taylor
Doador em destaque: Ricot Jean-Pierre, PAPDA
Doador em destaque: Miriam Miranda, OFRANEH
Formar de doar
Grupo de engajamento de doador-ativista
Conecte-se com a Grassroots
Home » News » Press Releases » 2013 Food Sovereignty Prize Honors Grassroots International Partners
2013 Food Sovereignty Prize Honors Grassroots International Partners
By: Mina Remy | Agosto 13, 2013
The US Food Sovereignty Alliance announced today their selection for the fifth annual Food Sovereignty Prize. This year's winners and honorable mentions include peasant organizations and movements that Grassroots International has supported over many years, even decades.
Grassroots International is a founding member of the US Food Sovereignty Alliance (USFSA). The USFSA – a growing alliance of farmer, farmworker, food chain worker, food justice, policy, advocacy, faith-based, and environmental/climate justice organizations both rural and urban – has been working since 2007 to build a movement for food sovereignty in the United States.
The USFSA instituted the Food Sovereignty Prize to both recognize efforts inside and outside the United States towards achieving our shared vision of food sovereignty and to raise awareness within the United States about these important issues.
Out of more than 40 nominations, the Alliance chose Haiti's Group of 4 (G4) and the peasant-to-peasant learning exchange, Dessalines Brigade, as this year's winner for their exemplary work in Haiti. Grassroots International has been a key financial supporter of the Dessalines Brigade since its inception in 2007. The G4 is an alliance between the four largest peasant movements in Haiti, two of which are Grassroots International partners (the Peasant Movement of Papaye and the National Congress of the Papaye Peasant Movement, while the other two are Grassroots International grantees (Tet Kole and CROSE).
Grassroots International also provided support to two Honorable Mention recipients as well: the Tamil Nadu Women's Collective (India) and the National Coordination of Peasant Organizations (Mali). The Basque Country Peasants' Solidarity of the Basque Country in Europe, a member of the Via Campesina, also received an honorable mention.
Grassroots International congratulates this year's winners and honorable mentions, and celebrates their leadership and power in supporting food sovereignty and human rights.
Below is the press release issued by the USFSA announcing this year's winners.
CONTACT: Charity Hicks, Detroit Food Justice Task Force, 313.725.0554 Lisa Griffith, National Family Farm Coalition, 800.639.3276 Christopher Cook, Food First, 415.504.0325
Food Sovereignty Prize Honors Grassroots Initiatives in Haiti, Brazil, Basque Country, Mali and India
FOR IMMEDIATE RELEASE–EMBARGOED UNTIL 1 PM EST, AUG. 13, 2013
NEW YORK CITY—Five innovative grassroots groups from across the globe working for democratic access to land, seeds, water and food have been honored with the 2013 Food Sovereignty Prize, the US Food Sovereignty Alliance announced today.
Winners of the fifth annual Food Sovereignty Prize were chosen from among more than 40 inspiring projects creating on-the-ground solutions to hunger and poverty, said the alliance, a network of food justice, anti-hunger, labor, environmental, faith-based, and food producer advocacy organizations.
Top honors go to the Haitian Group of 4 (G4) and the South American Dessalines Brigade, an international peasant-to-peasant collaboration working to rebuild Haiti's seed, soil and agricultural systems. Honorable mentions were garnered by Tamil Nadu Women's Collective of India; National Coordination of Peasant Organizations of Mali; and Basque Country Peasants' Solidarity of the Basque Country in Europe.
"The Food Sovereignty Prize symbolizes the fight for safe and healthy food for all peoples of the earth," said Chavannes Jean-Baptiste, G4 Executive Committee member. "It's a fight that must be waged both locally and globally, and requires deep solidarity among all organizations fighting for food sovereignty."
Flavio Barbosa, of the South American Dessalines Brigade, added: "Receiving this prize for the partnership between the Group of 4 and the Dessalines Brigade is an incentive for others to participate in long exchanges such as the one we are experiencing in Haiti. And it charges us with even greater responsibility to continue our defense of peasant agriculture and agroecology as a way to produce sustainable, healthy chemical-free foods accessible for all."
The US Food Sovereignty Alliance will present the awards at a ceremony in New York City on October 15, 2013, at the Smithsonian National Museum of the American Indian. The ceremony will be hosted by WhyHunger and feature keynote speaker Shirley Sherrod, the former USDA regional director forced to resign in 2010 after false accusations of racism. The evening will also feature musical entertainment. Subsequent events with the Food Sovereignty Prize honorees to highlight issues of food sovereignty in the US will take place in Des Moines, Iowa, and Detroit, Michigan, on October 16-21.
For event updates and background on food sovereignty and the prize winners, visit www.foodsovereigntyprize.org. On Twitter, #foodsovprize.
Since its launch in 2009, the Food Sovereignty Prize has garnered international attention for its recognition of community-based efforts that promote food democracy. In contrast to the World Food Prize, which emphasizes increased production through technology and this year rewarded scientists from transnational biotechnology corporations Monsanto and Syngenta, the Food Sovereignty Prize honors organizations and movements around the world fighting for the right to food for all people and dignity for those who put food on our plates.
"With this prize, we're honoring real-world, sustainable solutions to poverty, social instability and food insecurity," said Montana farmer Dena Hoff, North American Co-Chair of La Via Campesina, the first winner of the Food Sovereignty Prize in 2009. "With 40 nominations from 21 countries and a selection committee comprised of food justice activists, community leaders and academics from the US and Canada, the Food Sovereignty Prize recognizes effective and inspiring examples of communities making creative and truly lasting change in their food security–and in their democracy." Hoff is the Vice President of National Family Farm Coalition, a founding member of the US Food Sovereignty Alliance.
2013 Food Sovereignty Prize Honorees: Snapshots and Background
Winner: Group of 4, Dessalines Brigade/Via Campesina, Haiti & South America In 2007, Haiti's largest peasant organizations—Heads Together Small Farmers of Haiti (Tet Kole), the Peasant Movement of Papaye (MPP), the National Congress of Papaye Peasant Movements (MPNKP), and the Regional Coordination of Organizations of the South East Region (CROSE)—joined forces as the Group of 4 (G4), a national alliance to promote good farming practices and advocate for peasant farmers. The G4, representing over a quarter of a million Haitians, invited South American peasant leaders and agroecology experts to Haiti to work cooperatively to save Creole seeds and support peasant agriculture. Together, the G4 and the Dessalines Brigade, as it became known—named for 19th-century Haitian independence leader Jean Jacques Dessalines and supported by La Via Campesina—have collaborated to rebuild Haiti's environment, promote wealth and end poverty. The partnership also provided immediate and ongoing support to the victims of the 2010 earthquake, and the Group of 4 made global headlines when they rejected a donation of hybrid seeds from Monsanto.
Honorable Mention: Basque Country Peasants' Solidarity (EHNE), Basque Country In Europe's Basque Country, the struggle for food sovereignty is embedded in a broader struggle for political and cultural autonomy. A founder of the international peasant movement La Via Campesina in 1993, EHNE continues to be at the forefront of innovative and political food sovereignty approaches. Locally, EHNE offers its more than 6,000 members educational and economic support; its youth program has helped young people return to farming; and it is working to build new relationships between the countryside and regional cities. Due in part to the Basque Country's vibrant network of small farms, cooperative business and strong local food system, all supported by EHNE, the region has weathered the financial crisis better than much of Europe.
Honorable Mention: National Coordination of Peasant Organizations (CNOP), Mali As the national coordinating body of peasant organizations across Mali, CNOP represents some 2.5 million farmers. The organization works across the country to develop local, regional and national relationships between peasant and other rural development organizations, support family-based agriculture, lobby government and promote the needs of a national peasant movement, including significant efforts against land grabbing. In large part due to the efforts of CNOP to ensure that food sovereignty is the fundamental basis of Malian agricultural policy, Mali is one of only a few countries internationally to include the right to food sovereignty in its constitution.
Honorable Mention: Tamil Nadu Women's Collective (TNWC), India In the southern Indian state of Tamil Nadu, as in much of India, women have little social, economic or political power, and often struggle to feed their families. Lowest-caste Dalit women, indigenous, and widowed women face even greater hardships. Through the Tamil Nadu Women's Collective, 100,000 marginalized women are organized, many in unofficial worker unions or small collective farms, to strengthen their food sovereignty and thus their broader power. In addition to organizing locally and nationally on issues from their own families' food security to land rights to opposition to genetically modified seeds, the Collective encourages cultivation of native millet varieties – the hardy traditional grain is nutritious, drought-resistant, and easier to grow in the region than wheat or rice.


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Book Promotion: The X Factor
The opinions and words here are 100% mine! This post contains referral links that provide a reward for me at no cost to you if you choose to shop.
The X Factor: Confession of a Naïve Fashion Model
by Ivan Sivec
It takes three mistakes to be swallowed up and spat out by the fashion industry.
You trust the wrong people.
You think you know everything there is to know about life.
You abuse drugs.
The almost 18-year-old Maja is a regular high-school kid, who at the threshold of adulthood thinks she knows everything there is to know about life. As she enters the world of fashion modelling she gets completely enchanted.
However, the more her career thrives she's less able to see the danger lurking around fashion industry. Maja starts falling in a downward spiral of drug addiction and is torn between the tempting dream of becoming a world famous model and the horrendous thought of destroying her body beyond the point of no salvation.
At a yacht party Maja gets drugged, assaulted and tossed overboard. She's found the next day. Barely alive. Only a shade of her former self, in a gray space between life and death, she realises the fashion industry sucked everything out of her – except one thing – the will to live. Maja wants to be her old self again.
Ironically, it means losing the only thing that made her special. She has to give up on her hidden X Factor.
The X Factor: Confession of a Naïve Fashion Model is a psychological YA novel showing the glamorous fashion world behind its less glamorous scenes.
The window scene.
It was like a scene from a film about the escape of prisoners. They tied four sheets together to form a rope and climbed down onto the pavement, one by one. It was easy, just as if they were shooting a commercial. Downstairs, they hugged, as if they had just saved each other's lives.
"Nothing's open right now and we won't make it far on foot."
There was a bakery on the other side of the street. Every day they rushed past, as they were tempted by the wonderful smell coming out of it. They were too afraid they would give in to temptation.
"We'll break in to the shop, end of discussion!" Maja was overcome by a divine power. "As soon as we get our first salary, we'll pay the owner back everything, including a large tip. I'm sure he'll be understanding."
"But is there even any bread in there?"
"There must be! Old bread and bread rolls stay in the bakery until the morning, when the farmers from the suburbs pick them up. They must use them for pig fodder. I've watched them from our window many times."
The bakery wasn't built very solidly. They took out an old window, easily entered and rushed to the shelves.
"Wow, that's a lot of bread!"
"Oh, I'm so happy!"
Sitting on the floor in the middle of the room they stuffed themselves with old bread rolls until their stomachs became blown up as if they were pregnant.
"Well, I won't die after all!"
"I won't die either!"
Feeling blissfully satisfied, knowing they were full, they fell asleep from exhaustion and all the bad things that happened to them.
The owner found them half lying, half sitting, surrounded by old bread rolls.
"Mamma mia, mamma mia! What's the world coming to?" he said, wringing his hands.
"They're wearing the most expensive clothes in the world, but they would have died of hunger if they hadn't stolen some bread!"
He carefully woke them up and said with an understanding tone:
"Don't be afraid, I won't report you to the police. I've been observing you for a long time now. You're as thin as rakes! Are you even women still?! I wouldn't like to have such a woman for anything in the world. Oh, mamma mia, mamma mia, what's the world coming to, what's the world coming to…!"
They swore that when they get rich they would repay him for all the damage and add a prize on top.
"The biggest prize for me would be if you would buy some bread rolls each evening and start living a normal life."
But despite the fact they were full, they couldn't work that day. They were too full. They were so sick they vomited everything out, every last bite. They were too weak to even leave their beds. That was their first day without work. It was their own fault.
"A final warning before expulsion," the director Monica let them know. "There are at least a hundred girls waiting in line who are serious about work. One more mistake like that and you're out!"
Ivan Sivec is a writer, poet and publicist from Slovenia, Europe. People say he is 'a man with a hundred stories' and he has been sharing them with his audience, both young and mature, for over four decades now. He is listed under the top five Slovenian bestselling authors.
In his rich and thriving career he has written in many genres. As a poet he has written more than 2,500 lyrics for ethnic and entertaining music for which he received several awards. His broad prosaic 'repertoire' includes biographies, historical, psychological and adventure novels, travelogues, youth book series, picture books as well as humorous stories. To this date he published over 130 books and is considered to be one of the most popular Slovenian authors.
His particular interest goes to psychological novels on issues teenagers face today, especially topics like suicide, drug addiction, adoption, AIDS... He shares true stories to let the young reader know they're not alone.
Sivec devotes his free time to his wife of 44 years, their two children and three grandchildren. Since his retirement a decade ago he enjoys his days hiking the Alpine peaks (he is especially proud to have conquered the tallest mountain in Europe, the French Mont Blanc (4,810 m/ 15,771 ft), together with his daughter-in-law Tina). When he is not gathering inspiration from the mountains he is soaking in the tranquility of the Mediterranean Sea with his wife. Nature is his source of creativity. It allows Ivan to gather his thoughts and pour them onto paper.
Official Webpage
This book is free on Amazon on January 13 and 14.
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