Understanding deep learning requires rethinking generalization
Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family, or to the regularization techniques used during training. Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice. We interpret our experimental findings by comparison with traditional models.
This paper presents a set of experiments where via clever use of randomization and noise addition authors demonstrate the enormous modeling (memorization) power of the deep neural networks with large enough capacity. Yet these same models have very good generalization behavior even when all obvious explicit or implicit regularizers are removed. These observations are used to argue that classical theory (VC dimension, Rademacher complexity, uniform stability) is not able to explain the generalization behavior of deep neural networks, necessitating novel theory. This is a very interesting and thought provoking body of work and I am in complete accord with the observations and conclusions of the paper. The classical generalization theory is indeed often at a loss with complex enough model families. As the authors point out, once model families reach a point where they have capacity to memorize train sets, the classical theory does not yield useful results that could give insight into generalization behavior of these models, leaving one to empirical studies and observations. A minor clarification comment: On page 2, “ … true labels were replaced by random labels.” Please state that random labels came from the same set as the true labels, to clarify the experiment.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Understanding deep learning requires rethinking generalization
Despite their massive size, successful deep artificial neural networks can exhibit a remarkably small difference between training and test performance. Conventional wisdom attributes small generalization error either to properties of the model family, or to the regularization techniques used during training. Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simple depth two neural networks already have perfect finite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice. We interpret our experimental findings by comparison with traditional models.
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 presents a set of experiments where via clever use of randomization and noise addition authors demonstrate the enormous modeling (memorization) power of the deep neural networks with large enough capacity. Yet these same models have very good generalization behavior even when all obvious explicit or implicit regularizers are removed. These observations are used to argue that classical theory (VC dimension, Rademacher complexity, uniform stability) is not able to explain the generalization behavior of deep neural networks, necessitating novel theory. This is a very interesting and thought provoking body of work and I am in complete accord with the observations and conclusions of the paper. The classical generalization theory is indeed often at a loss with complex enough model families. As the authors point out, once model families reach a point where they have capacity to memorize train sets, the classical theory does not yield useful results that could give insight into generalization behavior of these models, leaving one to empirical studies and observations. A minor clarification comment: On page 2, “ … true labels were replaced by random labels.” Please state that random labels came from the same set as the true labels, to clarify the experiment.
assistant
This paper presents a set of experiments where via clever use of randomization and noise addition authors demonstrate the enormous modeling (memorization) power of the deep neural networks with large enough capacity. Yet these same models have very good generalization behavior even when all obvious explicit or implicit regularizers are removed. These observations are used to argue that classical theory (VC dimension, Rademacher complexity, uniform stability) is not able to explain the generalization behavior of deep neural networks, necessitating novel theory. This is a very interesting and thought provoking body of work and I am in complete accord with the observations and conclusions of the paper. The classical generalization theory is indeed often at a loss with complex enough model families. As the authors point out, once model families reach a point where they have capacity to memorize train sets, the classical theory does not yield useful results that could give insight into generalization behavior of these models, leaving one to empirical studies and observations. A minor clarification comment: On page 2, “ … true labels were replaced by random labels.” Please state that random labels came from the same set as the true labels, to clarify the experiment.