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 | 75 | 9.83% |
| Image Segmentation | 51 | 6.68% |
| Medical Image Segmentation | 34 | 4.46% |
| Denoising | 29 | 3.80% |
| Self-Supervised Learning | 28 | 3.67% |
| Image Classification | 28 | 3.67% |
| Object Detection | 21 | 2.75% |
| Classification | 21 | 2.75% |
| Tumor Segmentation | 10 | 1.31% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
