Regularization
Stable Rank Normalization
Introduced by Sanyal et al. in Stable Rank Normalization for Improved Generalization in Neural Networks and GANsStable Rank Normalization (SRN) is a weight-normalization scheme which minimizes the stable rank of a linear operator. It simultaneously controls the Lipschitz constant and the stable rank of a linear operator. Stable rank is a softer version of the rank operator and is defined as the squared ratio of the Frobenius norm to the spectral norm.
Source: Stable Rank Normalization for Improved Generalization in Neural Networks and GANs
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Decoder | 2 | 6.06% |
| Novel View Synthesis | 2 | 6.06% |
| Computational Efficiency | 2 | 6.06% |
| Image Reconstruction | 2 | 6.06% |
| Ensemble Learning | 1 | 3.03% |
| Few-Shot Learning | 1 | 3.03% |
| 3D Object Reconstruction | 1 | 3.03% |
| 3D Reconstruction | 1 | 3.03% |
| Object Reconstruction | 1 | 3.03% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
