Self-Supervised GAN Compression
Deep learning*s success has led to larger and larger models to handle more and more complex tasks; trained models can contain millions of parameters. These large models are compute- and memory-intensive, which makes it a challenge to deploy them with minimized latency, throughput, and storage requirements. Some model compression methods have been successfully applied on image classification and detection or language models, but there has been very little work compressing generative adversarial networks (GANs) performing complex tasks. In this paper, we show that a standard model compression technique, weight pruning, cannot be applied to GANs using existing methods. We then develop a self-supervised compression technique which uses the trained discriminator to supervise the training of a compressed generator. We show that this framework has a compelling performance to high degrees of sparsity, generalizes well to new tasks and models, and enables meaningful comparisons between different pruning granularities.
The problem tackled in the paper is the compression of generators in adversarially trained models. Considering the success of large generative models (like BigGAN) one may wonder if these models can be compressed after being trained to improve practical applicability. This paper is focused on the compression of image to image translation models and uses distillation on discriminator*s outputs to achieve better results. My decision is weak reject. The message of the article is misleading. The whole goal of pruning a neural network is to remove filters from it, therefore, reducing the computation or, at least, storage space for the parameters. The attempt to remove filters was presented in the last figure, and it does not work as good as all other results presented in the paper. The comparison in Figure 1 is arguably misleading as well. For example, one of the methods that were mentioned (LIT) does achieve a factor of 1.8 model compression, yet the comparison was not carried out directly with that method, but a modification proposed by the authors of this paper. I would like to see more comparisons in terms of FLOPs or inference time between the baselines, SotA methods, and your proposed method. Weights pruning is simply one of the approaches for model compression, so you cannot ignore the alternatives. Also, section 4 probably has to be rewritten, since some unorthodox notation is used. The authors should consider using some reference paper for the notations, like CycleGAN, that was mentioned in the paper. That will improve the clarity and readability of the used objectives.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Self-Supervised GAN Compression
Deep learning*s success has led to larger and larger models to handle more and more complex tasks; trained models can contain millions of parameters. These large models are compute- and memory-intensive, which makes it a challenge to deploy them with minimized latency, throughput, and storage requirements. Some model compression methods have been successfully applied on image classification and detection or language models, but there has been very little work compressing generative adversarial networks (GANs) performing complex tasks. In this paper, we show that a standard model compression technique, weight pruning, cannot be applied to GANs using existing methods. We then develop a self-supervised compression technique which uses the trained discriminator to supervise the training of a compressed generator. We show that this framework has a compelling performance to high degrees of sparsity, generalizes well to new tasks and models, and enables meaningful comparisons between different pruning granularities.
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 problem tackled in the paper is the compression of generators in adversarially trained models. Considering the success of large generative models (like BigGAN) one may wonder if these models can be compressed after being trained to improve practical applicability. This paper is focused on the compression of image to image translation models and uses distillation on discriminator*s outputs to achieve better results. My decision is weak reject. The message of the article is misleading. The whole goal of pruning a neural network is to remove filters from it, therefore, reducing the computation or, at least, storage space for the parameters. The attempt to remove filters was presented in the last figure, and it does not work as good as all other results presented in the paper. The comparison in Figure 1 is arguably misleading as well. For example, one of the methods that were mentioned (LIT) does achieve a factor of 1.8 model compression, yet the comparison was not carried out directly with that method, but a modification proposed by the authors of this paper. I would like to see more comparisons in terms of FLOPs or inference time between the baselines, SotA methods, and your proposed method. Weights pruning is simply one of the approaches for model compression, so you cannot ignore the alternatives. Also, section 4 probably has to be rewritten, since some unorthodox notation is used. The authors should consider using some reference paper for the notations, like CycleGAN, that was mentioned in the paper. That will improve the clarity and readability of the used objectives.
assistant
The problem tackled in the paper is the compression of generators in adversarially trained models. Considering the success of large generative models (like BigGAN) one may wonder if these models can be compressed after being trained to improve practical applicability. This paper is focused on the compression of image to image translation models and uses distillation on discriminator*s outputs to achieve better results. My decision is weak reject. The message of the article is misleading. The whole goal of pruning a neural network is to remove filters from it, therefore, reducing the computation or, at least, storage space for the parameters. The attempt to remove filters was presented in the last figure, and it does not work as good as all other results presented in the paper. The comparison in Figure 1 is arguably misleading as well. For example, one of the methods that were mentioned (LIT) does achieve a factor of 1.8 model compression, yet the comparison was not carried out directly with that method, but a modification proposed by the authors of this paper. I would like to see more comparisons in terms of FLOPs or inference time between the baselines, SotA methods, and your proposed method. Weights pruning is simply one of the approaches for model compression, so you cannot ignore the alternatives. Also, section 4 probably has to be rewritten, since some unorthodox notation is used. The authors should consider using some reference paper for the notations, like CycleGAN, that was mentioned in the paper. That will improve the clarity and readability of the used objectives.