Self-Supervised Learning
Jigsaw
Introduced by Noroozi et al. in Unsupervised Learning of Visual Representations by Solving Jigsaw PuzzlesJigsaw is a self-supervision approach that relies on jigsaw-like puzzles as the pretext task in order to learn image representations.
Source: Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Self-Supervised Learning | 24 | 13.71% |
| Classification | 9 | 5.14% |
| Image Classification | 8 | 4.57% |
| Semantic Segmentation | 8 | 4.57% |
| General Classification | 8 | 4.57% |
| Domain Generalization | 5 | 2.86% |
| Anomaly Detection | 5 | 2.86% |
| Domain Adaptation | 4 | 2.29% |
| Object Recognition | 4 | 2.29% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
