Pooling Operations
Max Pooling
Max Pooling is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.
Image Source: here
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 69 | 8.73% |
| Image Segmentation | 41 | 5.19% |
| Decoder | 35 | 4.43% |
| Denoising | 32 | 4.05% |
| Image Generation | 32 | 4.05% |
| Image Classification | 29 | 3.67% |
| Self-Supervised Learning | 22 | 2.78% |
| Medical Image Segmentation | 21 | 2.66% |
| Object Detection | 18 | 2.28% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
