Domain Adaptation
Source Hypothesis Transfer
Introduced by Liang et al. in Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain AdaptationSource Hypothesis Transfer, or SHOT, is a representation learning framework for unsupervised domain adaptation. SHOT freezes the classifier module (hypothesis) of the source model and learns the target-specific feature extraction module by exploiting both information maximization and self-supervised pseudo-labeling to implicitly align representations from the target domains to the source hypothesis.
Source: Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Domain Adaptation | 3 | 25.00% |
| Unsupervised Domain Adaptation | 2 | 16.67% |
| Classification | 1 | 8.33% |
| General Classification | 1 | 8.33% |
| Object Recognition | 1 | 8.33% |
| Self-Supervised Learning | 1 | 8.33% |
| Partial Domain Adaptation | 1 | 8.33% |
| Source-Free Domain Adaptation | 1 | 8.33% |
| Universal Domain Adaptation | 1 | 8.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
