Variation Network: Learning High-level Attributes for Controlled Input Manipulation
This paper presents the Variation Network (VarNet), a generative model providing means to manipulate the high-level attributes of a given input. The originality of our approach is that VarNet is not only capable of handling pre-defined attributes but can also learn the relevant attributes of the dataset by itself. These two settings can be easily combined which makes VarNet applicable for a wide variety of tasks. Further, VarNet has a sound probabilistic interpretation which grants us with a novel way to navigate in the latent spaces as well as means to control how the attributes are learned. We demonstrate experimentally that this model is capable of performing interesting input manipulation and that the learned attributes are relevant and interpretable.
 This paper introduces a new framework for learning an interpretable representation of images and their attributes. The authors suggest decomposing the representation into a set of *template* latent features, and a set of attribute-based features. The attribute-based features can be either *free*, i.e. discovered from the data, or *fixed*, i.e. based on the ground truth attributes. The authors encourage the decomposition of the latent space into the *template* and the *attributes* features by training a discriminator network to predict whether the attributes and the template features come from the same image or not. While the idea is interesting, the paper is lacking an experimental section, so the methodology is impossible to evaluate. Furthermore, while the authors spend many pages describing their methodology, the writing is often hard to follow, so I am still confused about the exact implementation of the attribute features .phi(x, m) for example. The authors do point to the Appendix for their Experiments section, however this is not a good idea. The paper should be self-contained and the authors should not assume that their readers will read the information presented in the Appendix, which is always optional. Unfortunately, even the experimental section presented in the Appendix is not comprehensive enough to evaluate the proposed method. The authors train the model on a single dataset (MNIST), no baseline or ablation results are presented, and all the results are purely qualitative. Given that the ground truth attribute decomposition for MNIST is not known, even the qualitative results are impossible to evaluate. I recommend that the authors present quantitive results in the updated version of their paper (i.e. disentanglement metric scores, the log-likelihood of the reconstructions), including new experiments on a dataset like dSprites or CelebA, where the ground truth attributes are known.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Variation Network: Learning High-level Attributes for Controlled Input Manipulation
This paper presents the Variation Network (VarNet), a generative model providing means to manipulate the high-level attributes of a given input. The originality of our approach is that VarNet is not only capable of handling pre-defined attributes but can also learn the relevant attributes of the dataset by itself. These two settings can be easily combined which makes VarNet applicable for a wide variety of tasks. Further, VarNet has a sound probabilistic interpretation which grants us with a novel way to navigate in the latent spaces as well as means to control how the attributes are learned. We demonstrate experimentally that this model is capable of performing interesting input manipulation and that the learned attributes are relevant and interpretable.
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 introduces a new framework for learning an interpretable representation of images and their attributes. The authors suggest decomposing the representation into a set of *template* latent features, and a set of attribute-based features. The attribute-based features can be either *free*, i.e. discovered from the data, or *fixed*, i.e. based on the ground truth attributes. The authors encourage the decomposition of the latent space into the *template* and the *attributes* features by training a discriminator network to predict whether the attributes and the template features come from the same image or not. While the idea is interesting, the paper is lacking an experimental section, so the methodology is impossible to evaluate. Furthermore, while the authors spend many pages describing their methodology, the writing is often hard to follow, so I am still confused about the exact implementation of the attribute features .phi(x, m) for example. The authors do point to the Appendix for their Experiments section, however this is not a good idea. The paper should be self-contained and the authors should not assume that their readers will read the information presented in the Appendix, which is always optional. Unfortunately, even the experimental section presented in the Appendix is not comprehensive enough to evaluate the proposed method. The authors train the model on a single dataset (MNIST), no baseline or ablation results are presented, and all the results are purely qualitative. Given that the ground truth attribute decomposition for MNIST is not known, even the qualitative results are impossible to evaluate. I recommend that the authors present quantitive results in the updated version of their paper (i.e. disentanglement metric scores, the log-likelihood of the reconstructions), including new experiments on a dataset like dSprites or CelebA, where the ground truth attributes are known.
assistant
 This paper introduces a new framework for learning an interpretable representation of images and their attributes. The authors suggest decomposing the representation into a set of *template* latent features, and a set of attribute-based features. The attribute-based features can be either *free*, i.e. discovered from the data, or *fixed*, i.e. based on the ground truth attributes. The authors encourage the decomposition of the latent space into the *template* and the *attributes* features by training a discriminator network to predict whether the attributes and the template features come from the same image or not. While the idea is interesting, the paper is lacking an experimental section, so the methodology is impossible to evaluate. Furthermore, while the authors spend many pages describing their methodology, the writing is often hard to follow, so I am still confused about the exact implementation of the attribute features .phi(x, m) for example. The authors do point to the Appendix for their Experiments section, however this is not a good idea. The paper should be self-contained and the authors should not assume that their readers will read the information presented in the Appendix, which is always optional. Unfortunately, even the experimental section presented in the Appendix is not comprehensive enough to evaluate the proposed method. The authors train the model on a single dataset (MNIST), no baseline or ablation results are presented, and all the results are purely qualitative. Given that the ground truth attribute decomposition for MNIST is not known, even the qualitative results are impossible to evaluate. I recommend that the authors present quantitive results in the updated version of their paper (i.e. disentanglement metric scores, the log-likelihood of the reconstructions), including new experiments on a dataset like dSprites or CelebA, where the ground truth attributes are known.