Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations

Venue

arXiv:1606.01305 [cs], vol.

Publication Year

2016

Keywords

Computer Science - Computation and Language,Computer Science - Machine Learning,Computer Science - Neural and Evolutionary Computing

Authors

  • David Krueger
  • Tegan Maharaj
  • János Kramár
  • Mohammad Pezeshki
  • Nicolas Ballas
  • Nan Rosemary Ke
  • Anirudh Goyal
  • Yoshua Bengio
  • Aaron Courville
  • Chris Pal

Abstract

We propose zoneout, a novel method for regularizing RNNs. At each timestep, zoneout stochastically forces some hidden units to maintain their previous values. Like dropout, zoneout uses random noise to train a pseudo-ensemble, improving generalization. But by preserving instead of dropping hidden units, gradient information and state information are more readily propagated through time, as in feedforward stochastic depth networks. We perform an empirical investigation of various RNN regularizers, and find that zoneout gives significant performance improvements across tasks. We achieve competitive results with relatively simple models in character- and word-level language modelling on the Penn Treebank and Text8 datasets, and combining with recurrent batch normalization yields state-of-the-art results on permuted sequential MNIST.