Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent

13 Feb 2018 Yifan Wu Barnabas Poczos Aarti Singh

A major challenge in understanding the generalization of deep learning is to explain why (stochastic) gradient descent can exploit the network architecture to find solutions that have good generalization performance when using high capacity models. We find simple but realistic examples showing that this phenomenon exists even when learning linear classifiers --- between two linear networks with the same capacity, the one with a convolutional layer can generalize better than the other when the data distribution has some underlying spatial structure... (read more)

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