Summary of Contrastive Adversarial Training For Unsupervised Domain Adaptation, by Jiahong Chen et al.
Contrastive Adversarial Training for Unsupervised Domain Adaptation
by Jiahong Chen, Zhilin Zhang, Lucy Li, Behzad Shahrasbi, Arjun Mishra
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel contrastive adversarial training (CAT) approach tackles the issue of domain biasedness in adversarial training for large vision transformer models on complex adaptation scenarios like DomainNet. Existing solutions focus on enhancing discriminators or improving backbone network stability, but fail to function well due to unbalanced competition between feature extractors and discriminators. CAT addresses three major challenges: ensuring indistinguishable feature distributions, encouraging target samples to move closer to the source in the feature space, and avoiding direct alignment of unpaired samples within mini-batches. This approach can be easily plugged into existing models and exhibits significant performance improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CAT is a new way to train models so they work well on different domains. Right now, big models like vision transformers are really good at doing certain tasks, but they often struggle when they’re used in different situations or environments. This is because they’re trained using lots of data from one place, and then expected to work well somewhere else without any more training. The problem is that the model becomes too specialized in what it learned from the first place, so it doesn’t generalize as well to new places. CAT helps by making sure the features (or building blocks) of the model are similar across different domains. This makes it easier for the model to adapt and learn when it’s used in a new environment. |
Keywords
» Artificial intelligence » Alignment » Vision transformer
