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 | 12 | 44.44% |
| Text-To-Speech Synthesis | 3 | 11.11% |
| Language Modelling | 3 | 11.11% |
| Style Transfer | 2 | 7.41% |
| General Classification | 1 | 3.70% |
| Speaker Verification | 1 | 3.70% |
| Speech Recognition | 1 | 3.70% |
| Meta-Learning | 1 | 3.70% |
| Disentanglement | 1 | 3.70% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
