Spontaneous Symmetry Breaking in Neural Networks

17 Oct 2017 Ricky Fok Aijun An Xiaogang Wang

We propose a framework to understand the unprecedented performance and robustness of deep neural networks using field theory. Correlations between the weights within the same layer can be described by symmetries in that layer, and networks generalize better if such symmetries are broken to reduce the redundancies of the weights... (read more)

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