Disentangling Trainability and Generalization in Deep Neural Networks

Jul 12, 2020



A longstanding goal in the theory of deep learn-ing is to characterize the conditions under whicha given neural network architecture will be train-able, and if so, how well it might generalize tounseen data. In this work, we provide such a char-acterization in the limit of very wide and verydeep networks, for which the analysis simplifiesconsiderably. For wide networks, the trajectoryunder gradient descent is governed by the NeuralTangent Kernel (NTK), and for deep networks,the NTK itself maintains only weak data depen-dence. By analyzing the spectrum of the NTK,we formulate necessary conditions for trainabilityand generalization across a range of architectures,including Fully Connected Networks (FCNs) andConvolutional Neural Networks (CNNs). Weidentify large regions of hyperparameter spacefor which networks can memorize the training setbut completely fail to generalize. We find thatCNNs without global average pooling behave al-most identically to FCNs, but that CNNs withpooling have markedly different and often bettergeneralization performance. A thorough empiri-cal investigation of these theoretical results showsexcellent agreement on real datasets.



About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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