Associate Normalization
Normalization is a key technique for training deep neural networks. It improves the stability of the training process and thus makes the networks easier to train. However, in typical normalization methods, the rescaling parameters that control the mean and variance of the output do not associate with any input information during the forward phase. Therefore, inputs of different types are treated as from the exact same distribution, which may limit the feature expressiveness of normalization module. We present Associate Normalization (AssocNorm) to overcome the above limitation. AssocNorm extracts the key information from input features and connects them with rescaling parameters by an auto-encoder-like neural network in the normalization module. Furthermore, AssocNorm normalizes the features of each example individually, so the accuracy is relatively stable for different batch sizes. The experimental results show that AssocNorm achieves better performance than Batch Normalization on several benchmark datasets under various hyper-parameter settings.
 The idea seems rather interesting and the experimental results encouraging (although -- small point-- I found your plotted curves for BatchNorm on Cifar-100 rather suspicious, there was no discontinuity at the 2nd learning rate reduction). Unfortunately I don*t think I can recommend to accept this paper as it was too un-clearly explained. I think the authors should try to get the involvement of someone with more machine learning experience who might be able to solidify their ideas.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Associate Normalization
Normalization is a key technique for training deep neural networks. It improves the stability of the training process and thus makes the networks easier to train. However, in typical normalization methods, the rescaling parameters that control the mean and variance of the output do not associate with any input information during the forward phase. Therefore, inputs of different types are treated as from the exact same distribution, which may limit the feature expressiveness of normalization module. We present Associate Normalization (AssocNorm) to overcome the above limitation. AssocNorm extracts the key information from input features and connects them with rescaling parameters by an auto-encoder-like neural network in the normalization module. Furthermore, AssocNorm normalizes the features of each example individually, so the accuracy is relatively stable for different batch sizes. The experimental results show that AssocNorm achieves better performance than Batch Normalization on several benchmark datasets under various hyper-parameter settings.
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 idea seems rather interesting and the experimental results encouraging (although -- small point-- I found your plotted curves for BatchNorm on Cifar-100 rather suspicious, there was no discontinuity at the 2nd learning rate reduction). Unfortunately I don*t think I can recommend to accept this paper as it was too un-clearly explained. I think the authors should try to get the involvement of someone with more machine learning experience who might be able to solidify their ideas.
assistant
 The idea seems rather interesting and the experimental results encouraging (although -- small point-- I found your plotted curves for BatchNorm on Cifar-100 rather suspicious, there was no discontinuity at the 2nd learning rate reduction). Unfortunately I don*t think I can recommend to accept this paper as it was too un-clearly explained. I think the authors should try to get the involvement of someone with more machine learning experience who might be able to solidify their ideas.