Domain Adaptation
DistanceNet
Introduced by Guo et al. in Multi-Source Domain Adaptation for Text Classification via DistanceNet-BanditsDistanceNet is a learning algorithm for multi-source domain adaptation that uses various distance measures, or a mixture of these distance measures, as an additional loss function to be minimized jointly with the task's loss function, so as to achieve better unsupervised domain adaptation.
Source: Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Classification | 1 | 16.67% |
| Domain Adaptation | 1 | 16.67% |
| General Classification | 1 | 16.67% |
| Sentiment Analysis | 1 | 16.67% |
| Text Classification | 1 | 16.67% |
| Unsupervised Domain Adaptation | 1 | 16.67% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
