Summary of Continual Domain Adversarial Adaptation Via Double-head Discriminators, by Yan Shen and Zhanghexuan Ji and Chunwei Ma and Mingchen Gao
Continual Domain Adversarial Adaptation via Double-Head Discriminators
by Yan Shen, Zhanghexuan Ji, Chunwei Ma, Mingchen Gao
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 authors address a challenge in continual learning, specifically domain adversarial adaptation with limited access to previous source domain data. They propose a double-head discriminator algorithm that introduces an additional source-only domain discriminator trained solely during the source learning phase. This approach reduces the empirical estimation error of -divergence-related adversarial loss from the source domain side. Experimental results show more than 2% improvement on all categories of target domain adaptation tasks while mitigating forgetting on the source domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in learning, where you can’t always go back to previous data to make decisions better. They create a new way to adapt old knowledge to new situations by introducing an extra “filter” that only looks at the original training data. This helps reduce mistakes and makes progress even with limited access to past information. |
Keywords
* Artificial intelligence * Continual learning * Domain adaptation
