Activation Functions
Rectified Linear Units
Rectified Linear Units, or ReLUs, are a type of activation function that are linear in the positive dimension, but zero in the negative dimension. The kink in the function is the source of the non-linearity. Linearity in the positive dimension has the attractive property that it prevents non-saturation of gradients (contrast with sigmoid activations), although for half of the real line its gradient is zero.
$$ f\left(x\right) = \max\left(0, x\right) $$
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 47 | 8.29% |
| Image Classification | 36 | 6.35% |
| General Classification | 28 | 4.94% |
| Object Detection | 27 | 4.76% |
| Image Generation | 14 | 2.47% |
| Denoising | 13 | 2.29% |
| Medical Image Segmentation | 12 | 2.12% |
| Domain Adaptation | 10 | 1.76% |
| Super-Resolution | 9 | 1.59% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |