Summary of Learn From the Learnt: Source-free Active Domain Adaptation Via Contrastive Sampling and Visual Persistence, by Mengyao Lyu et al.
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Persistence
by Mengyao Lyu, Tianxiang Hao, Xinhao Xu, Hui Chen, Zijia Lin, Jungong Han, Guiguang Ding
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 paper investigates a practical Domain Adaptation (DA) paradigm called Source data-Free Active Domain Adaptation (SFADA), where source data becomes inaccessible during adaptation, and only a limited annotation budget is available in the target domain. The authors present learn from the learnt (LFTL), a novel approach for SFADA that leverages knowledge from pre-trained models without extra overhead. They propose Contrastive Active Sampling to select informative target samples and Visual Persistence-guided Adaptation to facilitate feature distribution alignment. Extensive experiments on three benchmarks demonstrate state-of-the-art performance, superior computational efficiency, and continuous improvements as the annotation budget increases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a way to help machines learn from one place (the source domain) and apply what they’ve learned to another related area (the target domain), without needing all the original data. It’s called Source data-Free Active Domain Adaptation (SFADA). The researchers came up with a new idea, “learn from the learnt” (LFTL), which uses knowledge from pre-trained models to help machines learn even better. They also created a special way to pick the most helpful samples in the target domain and a method to make sure the machine is learning well. By testing their ideas on three different areas, they showed that it works really well and can do things faster than before. |
Keywords
* Artificial intelligence * Alignment * Domain adaptation
