Regularizing Neural Networks by Penalizing Confident Output Distributions
We propose regularizing neural networks by penalizing low entropy output distributions. We show that penalizing low entropy output distributions, which has been shown to improve exploration in reinforcement learning, acts as a strong regularizer in supervised learning. We connect our confidence penalty to label smoothing through the direction of the KL divergence between networks output distribution and the uniform distribution. We exhaustively evaluate our proposed confidence penalty and label smoothing (uniform and unigram) on 6 common benchmarks: image classification (MNIST and Cifar-10), language modeling (Penn Treebank), machine translation (WMT*14 English-to-German), and speech recognition (TIMIT and WSJ). We find that both label smoothing and our confidence penalty improve state-of-the-art models across benchmarks without modifying existing hyper-parameters.
The paper experimentally investigates a slightly modified version of label smoothing technique for neural network training, and reports results on various tasks. Such smoothing idea is not new, but was not investigated previously in wide range of machine learning tasks. Comments: The paper should report the state-of-the-art results for speech recognition tasks (TIMIT, WSJ), even if models are not directly comparable. The error back-propagation of label smoothing through softmax is straightforward and efficient. Is there an efficient solution for BP of the entropy smoothing through softmax? Although the classification accuracy could remain the same, the model will not estimate the true posterior distribution with this kind of smoothing. This might be an issue in complex machine learning problems where the decision is made on higher level and based on the posterior estimations, e.g. language models in speech recognition. More motivation is necessary for the proposed smoothing.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Regularizing Neural Networks by Penalizing Confident Output Distributions
We propose regularizing neural networks by penalizing low entropy output distributions. We show that penalizing low entropy output distributions, which has been shown to improve exploration in reinforcement learning, acts as a strong regularizer in supervised learning. We connect our confidence penalty to label smoothing through the direction of the KL divergence between networks output distribution and the uniform distribution. We exhaustively evaluate our proposed confidence penalty and label smoothing (uniform and unigram) on 6 common benchmarks: image classification (MNIST and Cifar-10), language modeling (Penn Treebank), machine translation (WMT*14 English-to-German), and speech recognition (TIMIT and WSJ). We find that both label smoothing and our confidence penalty improve state-of-the-art models across benchmarks without modifying existing hyper-parameters.
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 experimentally investigates a slightly modified version of label smoothing technique for neural network training, and reports results on various tasks. Such smoothing idea is not new, but was not investigated previously in wide range of machine learning tasks. Comments: The paper should report the state-of-the-art results for speech recognition tasks (TIMIT, WSJ), even if models are not directly comparable. The error back-propagation of label smoothing through softmax is straightforward and efficient. Is there an efficient solution for BP of the entropy smoothing through softmax? Although the classification accuracy could remain the same, the model will not estimate the true posterior distribution with this kind of smoothing. This might be an issue in complex machine learning problems where the decision is made on higher level and based on the posterior estimations, e.g. language models in speech recognition. More motivation is necessary for the proposed smoothing.
assistant
The paper experimentally investigates a slightly modified version of label smoothing technique for neural network training, and reports results on various tasks. Such smoothing idea is not new, but was not investigated previously in wide range of machine learning tasks. Comments: The paper should report the state-of-the-art results for speech recognition tasks (TIMIT, WSJ), even if models are not directly comparable. The error back-propagation of label smoothing through softmax is straightforward and efficient. Is there an efficient solution for BP of the entropy smoothing through softmax? Although the classification accuracy could remain the same, the model will not estimate the true posterior distribution with this kind of smoothing. This might be an issue in complex machine learning problems where the decision is made on higher level and based on the posterior estimations, e.g. language models in speech recognition. More motivation is necessary for the proposed smoothing.