Activation Functions
ReLU6
Introduced by Howard et al. in MobileNets: Efficient Convolutional Neural Networks for Mobile Vision ApplicationsReLU6 is a modification of the rectified linear unit where we limit the activation to a maximum size of $6$. This is due to increased robustness when used with low-precision computation.
Image Credit: PyTorch
Source: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 13 | 24.53% |
| Object Detection | 9 | 16.98% |
| Semantic Segmentation | 5 | 9.43% |
| Quantization | 2 | 3.77% |
| General Classification | 2 | 3.77% |
| Adversarial Attack | 1 | 1.89% |
| Speech Recognition | 1 | 1.89% |
| Multi-Label Learning | 1 | 1.89% |
| reinforcement-learning | 1 | 1.89% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
