Regularization
Zoneout
Introduced by Krueger et al. in Zoneout: Regularizing RNNs by Randomly Preserving Hidden ActivationsZoneout 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
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| 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% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
