Sampling Generative Networks
We introduce several techniques for sampling and visualizing the latent spaces of generative models. Replacing linear interpolation with spherical linear interpolation prevents diverging from a model*s prior distribution and produces sharper samples. J-Diagrams and MINE grids are introduced as visualizations of manifolds created by analogies and nearest neighbors. We demonstrate two new techniques for deriving attribute vectors: bias-corrected vectors with data replication and synthetic vectors with data augmentation. Binary classification using attribute vectors is presented as a technique supporting quantitative analysis of the latent space. Most techniques are intended to be independent of model type and examples are shown on both Variational Autoencoders and Generative Adversarial Networks.
This paper proposes a variety of techniques for visualizing learned generative models, focussing specifically on VAE and GAN models. This paper is somewhat challenging to assess since it doesn*t propose a new algorithm, model, application etc. On the one hand these techniques will be highly relevant to the generative modeling community and I think this paper deserves a wide audience. The techniques proposed are simple, well explained, and of immediate use to those working on generative models. However, I*m not sure the paper is appropriate for an ICLR conference track as it doesn*t provide any greater theoretical insights into sampling generative models and there are no comparisons / quantitative evaluations of the techniques proposed. Overall, I*m very much on the fence since I think the techniques are useful and this paper should be read by those interested in generating modeling. I would be willing to increase my core if the author could present a case for why ICLR is an appropriate venue for this work.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Sampling Generative Networks
We introduce several techniques for sampling and visualizing the latent spaces of generative models. Replacing linear interpolation with spherical linear interpolation prevents diverging from a model*s prior distribution and produces sharper samples. J-Diagrams and MINE grids are introduced as visualizations of manifolds created by analogies and nearest neighbors. We demonstrate two new techniques for deriving attribute vectors: bias-corrected vectors with data replication and synthetic vectors with data augmentation. Binary classification using attribute vectors is presented as a technique supporting quantitative analysis of the latent space. Most techniques are intended to be independent of model type and examples are shown on both Variational Autoencoders and Generative Adversarial Networks.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper proposes a variety of techniques for visualizing learned generative models, focussing specifically on VAE and GAN models. This paper is somewhat challenging to assess since it doesn*t propose a new algorithm, model, application etc. On the one hand these techniques will be highly relevant to the generative modeling community and I think this paper deserves a wide audience. The techniques proposed are simple, well explained, and of immediate use to those working on generative models. However, I*m not sure the paper is appropriate for an ICLR conference track as it doesn*t provide any greater theoretical insights into sampling generative models and there are no comparisons / quantitative evaluations of the techniques proposed. Overall, I*m very much on the fence since I think the techniques are useful and this paper should be read by those interested in generating modeling. I would be willing to increase my core if the author could present a case for why ICLR is an appropriate venue for this work.
assistant
This paper proposes a variety of techniques for visualizing learned generative models, focussing specifically on VAE and GAN models. This paper is somewhat challenging to assess since it doesn*t propose a new algorithm, model, application etc. On the one hand these techniques will be highly relevant to the generative modeling community and I think this paper deserves a wide audience. The techniques proposed are simple, well explained, and of immediate use to those working on generative models. However, I*m not sure the paper is appropriate for an ICLR conference track as it doesn*t provide any greater theoretical insights into sampling generative models and there are no comparisons / quantitative evaluations of the techniques proposed. Overall, I*m very much on the fence since I think the techniques are useful and this paper should be read by those interested in generating modeling. I would be willing to increase my core if the author could present a case for why ICLR is an appropriate venue for this work.