N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning
While bigger and deeper neural network architectures continue to advance the state-of-the-art for many computer vision tasks, real-world adoption of these networks is impeded by hardware and speed constraints. Conventional model compression methods attempt to address this problem by modifying the architecture manually or using pre-defined heuristics. Since the space of all reduced architectures is very large, modifying the architecture of a deep neural network in this way is a difficult task. In this paper, we tackle this issue by introducing a principled method for learning reduced network architectures in a data-driven way using reinforcement learning. Our approach takes a larger *teacher* network as input and outputs a compressed *student* network derived from the *teacher* network. In the first stage of our method, a recurrent policy network aggressively removes layers from the large *teacher* model. In the second stage, another recurrent policy network carefully reduces the size of each remaining layer. The resulting network is then evaluated to obtain a reward -- a score based on the accuracy and compression of the network. Our approach uses this reward signal with policy gradients to train the policies to find a locally optimal student network. Our experiments show that we can achieve compression rates of more than 10x for models such as ResNet-34 while maintaining similar performance to the input *teacher* network. We also present a valuable transfer learning result which shows that policies which are pre-trained on smaller *teacher* networks can be used to rapidly speed up training on larger *teacher* networks.
 On the positive side the paper is well written and the problem is interesting. On the negative side there is very limited innovation in the techniques proposed, that are indeed small variations of existing methods.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning
While bigger and deeper neural network architectures continue to advance the state-of-the-art for many computer vision tasks, real-world adoption of these networks is impeded by hardware and speed constraints. Conventional model compression methods attempt to address this problem by modifying the architecture manually or using pre-defined heuristics. Since the space of all reduced architectures is very large, modifying the architecture of a deep neural network in this way is a difficult task. In this paper, we tackle this issue by introducing a principled method for learning reduced network architectures in a data-driven way using reinforcement learning. Our approach takes a larger *teacher* network as input and outputs a compressed *student* network derived from the *teacher* network. In the first stage of our method, a recurrent policy network aggressively removes layers from the large *teacher* model. In the second stage, another recurrent policy network carefully reduces the size of each remaining layer. The resulting network is then evaluated to obtain a reward -- a score based on the accuracy and compression of the network. Our approach uses this reward signal with policy gradients to train the policies to find a locally optimal student network. Our experiments show that we can achieve compression rates of more than 10x for models such as ResNet-34 while maintaining similar performance to the input *teacher* network. We also present a valuable transfer learning result which shows that policies which are pre-trained on smaller *teacher* networks can be used to rapidly speed up training on larger *teacher* 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>



 On the positive side the paper is well written and the problem is interesting. On the negative side there is very limited innovation in the techniques proposed, that are indeed small variations of existing methods.
assistant
 On the positive side the paper is well written and the problem is interesting. On the negative side there is very limited innovation in the techniques proposed, that are indeed small variations of existing methods.