Summary of Vision Transformers in Domain Adaptation and Domain Generalization: a Study Of Robustness, by Shadi Alijani et al.
Vision transformers in domain adaptation and domain generalization: a study of robustness
by Shadi Alijani, Jamil Fayyad, Homayoun Najjaran
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper investigates the deployment of vision transformers in domain adaptation and domain generalization scenarios, building on their promising results in computer vision tasks. It categorizes research into various approaches, including feature-level, instance-level, model-level adaptations, and hybrid approaches for domain adaptation, as well as multi-domain learning, meta-learning, regularization techniques, and data augmentation strategies for domain generalization. The paper’s comprehensive tables summarize these categories, offering valuable insights for researchers. By leveraging vision transformers’ potential for robustness and generalization in handling distribution shifts, the work highlights their versatility in managing critical scenarios, such as safety and decision-making applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to use special computer models called vision transformers to make them work better in different situations. These models are good at recognizing pictures, but they can struggle when they’re shown new kinds of pictures they haven’t seen before. The researchers group different approaches into categories, like making the model learn from many types of pictures or using extra tricks to help it generalize. By understanding how these models can be used in different situations, we can make them more reliable and useful for important tasks. |
Keywords
» Artificial intelligence » Data augmentation » Domain adaptation » Domain generalization » Generalization » Meta learning » Regularization
