Methods › Computer Vision › Pooling Operations
Pooling Operations
The archive attaches this collection's text per method and the copies differ: 2 distinct texts across 15 of the 17 methods here. All are shown, most-carried first (a tie goes to the text carrying Papers with Code's collection boilerplate, then to the longer text); no vote is taken between them.
Text 1, carried by 14 of 17 methods:
Pooling Operations are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales).
Text 2, carried by 1 of 17 methods:
AutoML methods are used to automatically solve machine learning tasks without needing the user to specify or experiment with architectures, hyperparameters and other settings. Below you can find a continuously updating list of AutoML methods.
Also reached at /methods/category/pooling-operation (Papers with Code's slug for this collection; the archive carries no slugs, so this site's is derived from the name).
Methods
All 17 methods in this collection, most-tagged first. Year is the archive's introduced_year; the archive stores 2000 when it has none, shown here as “–”. Papers counts distinct papers the archive tags with the method. Click a heading to sort.
| Max Pooling | – | 7,126 |
| Average Pooling | – | 5,125 |
| Global Average Pooling | – | 4,076 |
| Spatial Pyramid Pooling | – | 285 |
| Cascade Corner Pooling | – | 46 |
| Center Pooling | – | 46 |
| Adaptive Feature Pooling | – | 20 |
| Corner Pooling | – | 12 |
| Generalized Mean Pooling | – | 6 |
| Strip Pooling | – | 6 |
| Class-MLP | – | 3 |
| Hopfield Layer | – | 3 |
| SoftPool Soft Pooling | – | 3 |
| Shape Adaptor | – | 2 |
| Local Importance-based Pooling | – | 1 |
| RMS Pooling Root-of-Mean-Squared Pooling | – | 0 |
| TFGW Template based Graph Neural Network with Optimal Transport Distances | – | 0 |