Summary of Zero-shot Domain Adaptation Based on Dual-level Mix and Contrast, by Yu Zhe et al.
Zero-shot domain adaptation based on dual-level mix and contrast
by Yu Zhe, Jun Sakuma
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Zero-shot Domain Adaptation (ZSDA) method learns domain-invariant features with low task bias, addressing the limitations of classical domain adaptation techniques. The approach consists of three components: data augmentation with dual-level mixups in both task and domain to fill the absence of target task-of-interest data; an extension of domain adversarial learning to learn domain-invariant features with less task bias; and a new dual-level contrastive learning method that enhances domain-invariance and less task biasedness of features. Experimental results demonstrate the effectiveness of this proposal on several benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem in machine learning called zero-shot domain adaptation. It’s like trying to learn about a new topic just by looking at different books, but some of those books are about completely different topics! The researchers came up with a way to make sure the features they learn aren’t too tied to one specific topic or book. They use three techniques: mixing up data from different sources and tasks, making the model less focused on a particular task, and contrasting features to make them more general. This helps the model perform well even when it hasn’t seen the exact same types of data before. |
Keywords
* Artificial intelligence * Data augmentation * Domain adaptation * Machine learning * Zero shot
