Summary of Domain Adaptation with Cauchy-schwarz Divergence, by Wenzhe Yin et al.
Domain Adaptation with Cauchy-Schwarz Divergence
by Wenzhe Yin, Shujian Yu, Yicong Lin, Jie Liu, Jan-Jakob Sonke, Efstratios Gavves
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 introduces a novel approach to unsupervised domain adaptation (UDA) called Cauchy-Schwarz (CS) divergence. The authors demonstrate that CS divergence provides a tighter generalization error bound compared to the Kullback-Leibler divergence for both multi-class classification and regression tasks. They also show that CS divergence can be used to estimate the discrepancy between source and target domains in the representation space without assuming specific distributions. This approach is applicable to distance metric- or adversarial training-based UDA frameworks, leading to improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way to help machines learn from different types of data. It’s called domain adaptation, and it helps machines understand how to apply what they’ve learned from one type of data to another similar type. To do this, the authors introduce a new measure called Cauchy-Schwarz divergence that can be used to compare how well machines are learning from different datasets. This approach is useful for tasks like image recognition or language processing. |
Keywords
» Artificial intelligence » Classification » Domain adaptation » Generalization » Regression » Unsupervised
