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 | 15 | 17.05% |
| Object Detection | 10 | 11.36% |
| Quantization | 5 | 5.68% |
| Semantic Segmentation | 5 | 5.68% |
| Classification | 4 | 4.55% |
| Test | 3 | 3.41% |
| Neural Network Compression | 2 | 2.27% |
| Bayesian Optimization | 2 | 2.27% |
| Network Pruning | 2 | 2.27% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
