Parametrizing filters of a CNN with a GAN
It is commonly agreed that the use of relevant invariances as a good statistical bias is important in machine-learning. However, most approaches that explicitely incorporate invariances into a model architecture only make use of very simple transformations, such as translations and rotations. Hence, there is a need for methods to model and extract richer transformations that capture much higher-level invariances. To that end, we introduce a tool allowing to parametrize the set of filters of a trained convolutional neural network with the latent space of a generative adversarial network. We then show that the method can capture highly non-linear invariances of the data by visualizing their effect in the data space.
 The paper proposes an approach to learning a distribution over filters of a CNN. The method is based on a adversarial training: the generator produces filters, and the discriminator aims to distinguish the activation maps produced by real filters from those produced by the generated ones. Pros: 1) The general task of learning distributions over network weights is interesting 2) To my knowledge, the proposed approach is new Cons: 1) Experimental evaluation is very substandard. The experiments on invariances seem to be the highlight of the paper, but they basically do not tell me anything. - Figures 3 and 4 take 2 pages, but what should one see there? - There are no quantitative results. Could there be a way to measure the invariances? - Can the results be applied to some practical task? Why are the results interesting and/or useful? 2) The experiments are restricted to a single dataset - MNIST. The authors mention that “the test accuracy obtained by following the above procedure is of 0.982, against a test accuracy of 0.971 for the real CNN” - these are very poor accuracies for MNIST. So even the MNIST results do not seem convincing. 3) Presentation is suboptimal, and many details are missing. For instance, architectures of networks are not provided. To conclude, while the general direction is interesting and the proposed method might work, the experimental evaluation is very poor, and the paper absolutely cannot be accepted for publication.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Parametrizing filters of a CNN with a GAN
It is commonly agreed that the use of relevant invariances as a good statistical bias is important in machine-learning. However, most approaches that explicitely incorporate invariances into a model architecture only make use of very simple transformations, such as translations and rotations. Hence, there is a need for methods to model and extract richer transformations that capture much higher-level invariances. To that end, we introduce a tool allowing to parametrize the set of filters of a trained convolutional neural network with the latent space of a generative adversarial network. We then show that the method can capture highly non-linear invariances of the data by visualizing their effect in the data space.
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>



 The paper proposes an approach to learning a distribution over filters of a CNN. The method is based on a adversarial training: the generator produces filters, and the discriminator aims to distinguish the activation maps produced by real filters from those produced by the generated ones. Pros: 1) The general task of learning distributions over network weights is interesting 2) To my knowledge, the proposed approach is new Cons: 1) Experimental evaluation is very substandard. The experiments on invariances seem to be the highlight of the paper, but they basically do not tell me anything. - Figures 3 and 4 take 2 pages, but what should one see there? - There are no quantitative results. Could there be a way to measure the invariances? - Can the results be applied to some practical task? Why are the results interesting and/or useful? 2) The experiments are restricted to a single dataset - MNIST. The authors mention that “the test accuracy obtained by following the above procedure is of 0.982, against a test accuracy of 0.971 for the real CNN” - these are very poor accuracies for MNIST. So even the MNIST results do not seem convincing. 3) Presentation is suboptimal, and many details are missing. For instance, architectures of networks are not provided. To conclude, while the general direction is interesting and the proposed method might work, the experimental evaluation is very poor, and the paper absolutely cannot be accepted for publication.
assistant
 The paper proposes an approach to learning a distribution over filters of a CNN. The method is based on a adversarial training: the generator produces filters, and the discriminator aims to distinguish the activation maps produced by real filters from those produced by the generated ones. Pros: 1) The general task of learning distributions over network weights is interesting 2) To my knowledge, the proposed approach is new Cons: 1) Experimental evaluation is very substandard. The experiments on invariances seem to be the highlight of the paper, but they basically do not tell me anything. - Figures 3 and 4 take 2 pages, but what should one see there? - There are no quantitative results. Could there be a way to measure the invariances? - Can the results be applied to some practical task? Why are the results interesting and/or useful? 2) The experiments are restricted to a single dataset - MNIST. The authors mention that “the test accuracy obtained by following the above procedure is of 0.982, against a test accuracy of 0.971 for the real CNN” - these are very poor accuracies for MNIST. So even the MNIST results do not seem convincing. 3) Presentation is suboptimal, and many details are missing. For instance, architectures of networks are not provided. To conclude, while the general direction is interesting and the proposed method might work, the experimental evaluation is very poor, and the paper absolutely cannot be accepted for publication.