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arXiv · 1706.08947

Exploring Generalization in Deep Learning

Behnam Neyshabur·Srinadh Bhojanapalli·David McAllester·Nathan Srebro
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Abstract

With a goal of understanding what drives generalization in deep networks, we consider several recently suggested explanations, including norm-based control, sharpness and robustness. We study how these measures can ensure generalization, highlighting the importance of scale normalization, and making a connection between sharpness and PAC-Bayes theory. We then investigate how well the measures explain different observed phenomena.

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Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro. 2017-06-27. Exploring Generalization in Deep Learning. https://arxiv.org/abs/1706.08947

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