Convolutions
Pointwise Convolution
Pointwise Convolution is a type of convolution that uses a 1x1 kernel: a kernel that iterates through every single point. This kernel has a depth of however many channels the input image has. It can be used in conjunction with depthwise convolutions to produce an efficient class of convolutions known as depthwise-separable convolutions.
Image Credit: Chi-Feng Wang
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 70 | 10.54% |
| Object Detection | 48 | 7.23% |
| Classification | 37 | 5.57% |
| Semantic Segmentation | 32 | 4.82% |
| Quantization | 30 | 4.52% |
| Instance Segmentation | 11 | 1.66% |
| Management | 9 | 1.36% |
| Ensemble Learning | 8 | 1.20% |
| Computational Efficiency | 8 | 1.20% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
