Activation Functions
Hard Swish
Introduced by Howard et al. in Searching for MobileNetV3Hard Swish is a type of activation function based on Swish, but replaces the computationally expensive sigmoid with a piecewise linear analogue:
$$\text{h-swish}\left(x\right) = x\frac{\text{ReLU6}\left(x+3\right)}{6} $$
Source: Searching for MobileNetV3
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 12 | 25.00% |
| Object Detection | 8 | 16.67% |
| Semantic Segmentation | 4 | 8.33% |
| Quantization | 2 | 4.17% |
| General Classification | 2 | 4.17% |
| Adversarial Attack | 1 | 2.08% |
| Speech Recognition | 1 | 2.08% |
| Multi-Label Learning | 1 | 2.08% |
| reinforcement-learning | 1 | 2.08% |
ReLU6
