Normalization
EvoNorms
Introduced by Liu et al. in Evolving Normalization-Activation LayersEvoNorms are a set of normalization-activation layers that go beyond existing design patterns. Normalization and activation are unified into a single computation graph, its structure is evolved starting from low-level primitives. EvoNorms consist of two series: B series and S series. The B series are batch-dependent and were discovered by our method without any constraint. The S series work on individual samples, and were discovered by rejecting any batch-dependent operations.
Source: Evolving Normalization-Activation Layers
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 1 | 25.00% |
| Image Generation | 1 | 25.00% |
| Instance Segmentation | 1 | 25.00% |
| Semantic Segmentation | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
