Transcription of Understanding deep learning requires rethinking …
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Understanding deep learning requires rethinking …
Understanding deep learning requires RE-THINKING GENERALIZATIONC hiyuan Zhang Massachusetts Institute of BengioGoogle HardtGoogle Recht University of California, VinyalsGoogle their massive size, successful deep artificial neural networks can exhibit aremarkably small difference between training and test performance. Conventionalwisdom attributes small generalization error either to properties of the model fam-ily, or to the regularization techniques used during extensive systematic experiments, we show how these traditional ap-proaches fail to explain why large neural networks generalize well in , our experiments establish that state-of-the-art convolutional networksfor image classification trained with stochastic gradient methods easily fit a ran-dom labeling of the training data.
A previous construction due to Livni et al. (2014) achieved a similar result with far more parameters, namely, O (dn ): While our depth 2 network inevitably has large width, we can also come up with a depth k network in which ... Understanding deep learning requires rethinking generalization ...
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