Self-Supervised Learning
RotNet
Introduced by Johnson et al. in RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural NetworksRotNet is a self-supervision approach that relies on predicting image rotations as the pretext task in order to learn image representations.
Source: RotNet: Fast and Scalable Estimation of Stellar Rotation Periods Using Convolutional Neural Networks
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Domain Adaptation | 1 | 10.00% |
| Image Classification | 1 | 10.00% |
| Image Retrieval | 1 | 10.00% |
| Retrieval | 1 | 10.00% |
| Self-Supervised Learning | 1 | 10.00% |
| Semantic Segmentation | 1 | 10.00% |
| Sketch-Based Image Retrieval | 1 | 10.00% |
| Open Set Learning | 1 | 10.00% |
| Feature Engineering | 1 | 10.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
