Understanding deep learning requires rethinking generalization

ICLR, Volume abs/1611.03530, 2017.

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These models are in principle rich enough to memorize the training data. This situation poses a conceptual challenge to statistical learning theory as traditional measures of model complexity struggle to explain the generalization ability of large artificial neural networks

Abstract:

Despite their massive size, successful deep artificial neural networks canexhibit a remarkably small difference between training and test performance.Conventional wisdom attributes small generalization error either to propertiesof the model family, or to the regularization techniques used during training.Through extensive systematic exper...More

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