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Deep double descent: where bigger models and more data hurt
We show that a variety of modern deep learning tasks exhibit a ‘double-descent’ phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of t...
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Published in: | Journal of statistical mechanics 2021-12, Vol.2021 (12), p.124003 |
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Main Authors: | , , , , , |
Format: | Article |
Language: | English |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | We show that a variety of modern deep learning tasks exhibit a ‘double-descent’ phenomenon where, as we increase model size, performance first gets
worse
and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We unify the above phenomena by defining a new complexity measure we call the
effective model complexity
and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually
hurts
test performance. |
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ISSN: | 1742-5468 1742-5468 |
DOI: | 10.1088/1742-5468/ac3a74 |