Summary of Gradient Harmonization in Unsupervised Domain Adaptation, by Fuxiang Huang et al.
Gradient Harmonization in Unsupervised Domain Adaptation
by Fuxiang Huang, Suqi Song, Lei Zhang
First submitted to arxiv on: 1 Aug 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 introduces two solutions, Gradient Harmonization (GH) and its improved version GH++, to address the challenge of simultaneously optimizing domain alignment and classification tasks in unsupervised domain adaptation (UDA). Current methods often overlook this conflict between tasks during gradient-based optimization. The proposed approaches harmonize gradients by adjusting angles from obtuse to acute or vertical, minimizing deviation from original directions. A dynamically weighted loss function is also evolved for optimization convenience and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps machines learn to transfer knowledge from one place to another without labeled information. It solves a problem where different tasks compete with each other during learning. The solutions make these tasks work together better by adjusting how they change when trying to find the best answers. This makes current methods that do UDA work even better. |
Keywords
» Artificial intelligence » Alignment » Classification » Domain adaptation » Loss function » Optimization » Unsupervised
