Exploring Generalization in Deep Learning
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.
- Publication:
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arXiv e-prints
- Pub Date:
- June 2017
- DOI:
- 10.48550/arXiv.1706.08947
- arXiv:
- arXiv:1706.08947
- Bibcode:
- 2017arXiv170608947N
- Keywords:
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- Computer Science - Machine Learning
- E-Print:
- 19 pages, 8 figures