Summary of Glc++: Source-free Universal Domain Adaptation Through Global-local Clustering and Contrastive Affinity Learning, by Sanqing Qu et al.
GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning
by Sanqing Qu, Tianpei Zou, Florian Röhrbein, Cewu Lu, Guang Chen, Dacheng Tao, Changjun Jiang
First submitted to arxiv on: 21 Mar 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 This research proposes a solution to improve deep neural networks’ performance under various shifts, specifically exploring Source-Free Universal Domain Adaptation (SF-UniDA). The authors develop a novel Global and Local Clustering (GLC) technique for classifying known data from target-private unknown data. GLC is enhanced with contrastive affinity learning, allowing it to identify distinct unknown categories more effectively. Experimental results demonstrate GLC’s superiority across multiple benchmarks, including VisDA, where it outperforms GATE by 16.7%. This novel approach has significant implications for enhancing domain adaptation and boosting performance in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps improve how computers can understand data that changes or is different from what they’ve seen before. The scientists developed a new way to group similar data together, called Global and Local Clustering (GLC). This method is better than previous approaches at recognizing new categories of data it has never seen before. By making computers more accurate in these situations, this research can help us create more effective artificial intelligence tools for everyday use. |
Keywords
* Artificial intelligence * Boosting * Clustering * Domain adaptation
