Web: http://arxiv.org/abs/2209.10080

Sept. 22, 2022, 1:11 a.m. | Matteo Gamba, Erik Englesson, Mårten Björkman, Hossein Azizpour

cs.LG updates on arXiv.org arxiv.org

Overparameterized deep networks are known to be able to perfectly fit the
training data while at the same time showing good generalization performance. A
common paradigm drawn from intuition on linear regression suggests that large
networks are able to interpolate even noisy data, without considerably
deviating from the ground-truth signal. At present, a precise characterization
of this phenomenon is missing. In this work, we present an empirical study of
sharpness of the loss landscape of deep networks as we systematically …


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