Zoneout is a 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.

Source: Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations


Paper Code Results Date Stars


Task Papers Share
Speech Synthesis 14 34.15%
Text-To-Speech Synthesis 4 9.76%
Language Modelling 3 7.32%
Voice Cloning 2 4.88%
Style Transfer 2 4.88%
Acoustic Modelling 1 2.44%
Voice Conversion 1 2.44%
Transliteration 1 2.44%
Zero-Shot Learning 1 2.44%


Component Type
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