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 | 63 | 9.89% |
| Image Classification | 35 | 5.49% |
| Medical Image Segmentation | 27 | 4.24% |
| Self-Supervised Learning | 20 | 3.14% |
| Object Detection | 20 | 3.14% |
| Tumor Segmentation | 14 | 2.20% |
| Denoising | 12 | 1.88% |
| Quantization | 12 | 1.88% |
| Computed Tomography (CT) | 12 | 1.88% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
