Normalization
Activation Normalization
Introduced by Kingma et al. in Glow: Generative Flow with Invertible 1x1 ConvolutionsActivation Normalization is a type of normalization used for flow-based generative models; specifically it was introduced in the GLOW architecture. An ActNorm layer performs an affine transformation of the activations using a scale and bias parameter per channel, similar to batch normalization. These parameters are initialized such that the post-actnorm activations per-channel have zero mean and unit variance given an initial minibatch of data. This is a form of data dependent initilization. After initialization, the scale and bias are treated as regular trainable parameters that are independent of the data.
Source: Glow: Generative Flow with Invertible 1x1 Convolutions
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Flare Removal | 3 | 7.50% |
| Image Enhancement | 3 | 7.50% |
| Federated Learning | 2 | 5.00% |
| Denoising | 2 | 5.00% |
| Low-Light Image Enhancement | 2 | 5.00% |
| Zero-Shot Learning | 2 | 5.00% |
| Image Dehazing | 2 | 5.00% |
| Text to Speech | 2 | 5.00% |
| Intrusion Detection | 1 | 2.50% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
