Summary of Domain Adversarial Active Learning For Domain Generalization Classification, by Jianting Chen et al.
Domain Adversarial Active Learning for Domain Generalization Classification
by Jianting Chen, Ling Ding, Yunxiao Yang, Zaiyuan Di, Yang Xiang
First submitted to arxiv on: 10 Mar 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 This paper explores domain generalization models, which aim to learn from source domains and apply this knowledge to unknown target domains. Research has shown that diverse and rich source samples enhance domain generalization capabilities. This study argues that each sample’s impact on the model’s generalization ability varies. A high-quality dataset can still achieve some level of generalization despite its small scale. To address this, the authors propose a domain-adversarial active learning (DAAL) algorithm for classification tasks in domain generalization. The DAAL algorithm prioritizes challenging samples and identifies feature subsets lacking discriminatory power within each domain, optimizing them with a constraint loss. The authors validate their algorithm on multiple datasets, comparing it to various domain generalization and active learning algorithms. Results show that DAAL can achieve strong generalization ability with fewer data resources, reducing annotation costs in domain generalization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about machines that learn from one set of things (source domains) and then use that knowledge to do well on new sets of things (target domains). Researchers have found that having many different types of source samples helps the machine generalize better. But what if some of those samples are more important than others? This study says yes, and proposes a new way for the machine to choose which samples to learn from. The method is called DAAL, short for domain-adversarial active learning. It picks the most challenging samples and figures out which features in each domain aren’t very helpful. By doing this, the algorithm can achieve good results with less data. This means it might be more efficient and cost-effective. |
Keywords
* Artificial intelligence * Active learning * Classification * Domain generalization * Generalization
