Equi-normalization of Neural Networks

Pierre Stock, Benjamin Graham, Rémi Gribonval, Hervé Jégou

Sep 27, 2018 ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: Modern neural networks are over-parametrized. In particular, each rectified linear hidden unit can be modified by a multiplicative factor by adjusting input and out- put weights, without changing the rest of the network. Inspired by the Sinkhorn-Knopp algorithm, we introduce a fast iterative method for minimizing the l2 norm of the weights, equivalently the weight decay regularizer. It provably converges to a unique solution. Interleaving our algorithm with SGD during training improves the test accuracy. For small batches, our approach offers an alternative to batch- and group- normalization on CIFAR-10 and ImageNet with a ResNet-18.
  • Keywords: convolutional neural networks, Normalization, Sinkhorn, Regularization
  • TL;DR: Fast iterative algorithm to balance the energy of a network while staying in the same functional equivalence class
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