Methods › General › Domain Adaptation
Domain Adaptation
Generative Adversarial Networks (GANs) are a type of generative model that use two networks, a generator to generate images and a discriminator to discriminate between real and fake, to train a model that approximates the distribution of the data. Below you can find a continuously updating list of GANs.
Methods
All 18 methods in this collection, most-tagged first. Year is the archive's introduced_year; the archive stores 2000 when it has none, shown here as “–”. Papers counts distinct papers the archive tags with the method. Click a heading to sort.
| CORAL Correlation Alignment for Deep Domain Adaptation | – | 126 |
| DANCE Domain Adaptative Neighborhood Clustering via Entropy Optimization | – | 10 |
| PGA Prompt Gradient Alignment | – | 5 |
| Source Hypothesis Transfer | – | 3 |
| L2M Learning to Match | – | 2 |
| MSGAN Multi-source Sentiment Generative Adversarial Network | – | 2 |
| Mechanism Transfer | – | 2 |
| SIFA Synergistic Image and Feature Alignment | – | 2 |
| SSTDA Self-Supervised Temporal Domain Adaptation | – | 2 |
| SymmNet Domain-Symmetric Network | – | 2 |
| Tunable Network | – | 2 |
| ALDI++ Align and Distill ++ | – | 1 |
| DAEL Domain Adaptive Ensemble Learning | – | 1 |
| DistanceNet | – | 1 |
| MPGA Multi Prompt Gradient Alignment | – | 1 |
| SGPCS Self-training Guided Prototypical Cross-domain Self-supervised learning | – | 1 |
| SRDC Structurally Regularized Deep Clustering | – | 1 |
| STMDA-RetinaNet Self training multi target domain adaptive RetinaNet | – | 1 |