Differentiable Learning-to-Normalize via Switchable Normalization
We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN switches between them by learning their importance weights in an end-to-end manner. It has several good properties. First, it adapts to various network architectures and tasks (see Fig.1). Second, it is robust to a wide range of batch sizes, maintaining high performance even when small minibatch is presented (e.g. 2 images/GPU). Third, SN does not have sensitive hyper-parameter, unlike group normalization that searches the number of groups as a hyper-parameter. Without bells and whistles, SN outperforms its counterparts on various challenging benchmarks, such as ImageNet, COCO, CityScapes, ADE20K, and Kinetics. Analyses of SN are also presented. We hope SN will help ease the usage and understand the normalization techniques in deep learning. The code of SN will be released.
 This paper considers normalization by learning to average across well-known normalization techniques. This meta-procedure almost by definition can yield better results, and this is demonstrated nicely by the authors. The idea is simple and easy to implement, and easy works in this case. I would like to hear more about the connections to other meta-learning procedures, by expanding the discussion on page 3. That discussion is very interesting but quite short, and I am afraid I can*t see how SN avoids overfitting compared to other approaches. Also, the section on Inference in page 4 is unclear to me. What parameters are being inferred and why is the discussion focused on convergence instead?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Differentiable Learning-to-Normalize via Switchable Normalization
We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN switches between them by learning their importance weights in an end-to-end manner. It has several good properties. First, it adapts to various network architectures and tasks (see Fig.1). Second, it is robust to a wide range of batch sizes, maintaining high performance even when small minibatch is presented (e.g. 2 images/GPU). Third, SN does not have sensitive hyper-parameter, unlike group normalization that searches the number of groups as a hyper-parameter. Without bells and whistles, SN outperforms its counterparts on various challenging benchmarks, such as ImageNet, COCO, CityScapes, ADE20K, and Kinetics. Analyses of SN are also presented. We hope SN will help ease the usage and understand the normalization techniques in deep learning. The code of SN will be released.
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 considers normalization by learning to average across well-known normalization techniques. This meta-procedure almost by definition can yield better results, and this is demonstrated nicely by the authors. The idea is simple and easy to implement, and easy works in this case. I would like to hear more about the connections to other meta-learning procedures, by expanding the discussion on page 3. That discussion is very interesting but quite short, and I am afraid I can*t see how SN avoids overfitting compared to other approaches. Also, the section on Inference in page 4 is unclear to me. What parameters are being inferred and why is the discussion focused on convergence instead?
assistant
 This paper considers normalization by learning to average across well-known normalization techniques. This meta-procedure almost by definition can yield better results, and this is demonstrated nicely by the authors. The idea is simple and easy to implement, and easy works in this case. I would like to hear more about the connections to other meta-learning procedures, by expanding the discussion on page 3. That discussion is very interesting but quite short, and I am afraid I can*t see how SN avoids overfitting compared to other approaches. Also, the section on Inference in page 4 is unclear to me. What parameters are being inferred and why is the discussion focused on convergence instead?