Interpretability
Class-activation map
Introduced by Oquab et al. in Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural NetworksClass activation maps could be used to interpret the prediction decision made by the convolutional neural network (CNN).
Image source: Learning Deep Features for Discriminative Localization
Source: Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Semantic Segmentation | 66 | 13.25% |
| Weakly-Supervised Semantic Segmentation | 53 | 10.64% |
| Object Localization | 27 | 5.42% |
| Object | 27 | 5.42% |
| Image Classification | 20 | 4.02% |
| Weakly-Supervised Object Localization | 20 | 4.02% |
| Classification | 15 | 3.01% |
| General Classification | 11 | 2.21% |
| Decision Making | 9 | 1.81% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
