Unpaired Image-to-Image Translation
ALDA
Introduced by Chen et al. in Adversarial-Learned Loss for Domain AdaptationAdversarial-Learned Loss for Domain Adaptation is a method for domain adaptation that combines adversarial learning with self-training. Specifically, the domain discriminator has to produce different corrected labels for different domains, while the feature generator aims to confuse the domain discriminator. The adversarial process finally leads to a proper confusion matrix on the target domain. In this way, ALDA takes the strengths of domain-adversarial learning and self-training based methods.
Source: Adversarial-Learned Loss for Domain Adaptation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Domain Adaptation | 2 | 50.00% |
| Unsupervised Domain Adaptation | 1 | 25.00% |
| Pseudo Label | 1 | 25.00% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
