Combating Label Distribution Shift for Active Domain Adaptation
- Title
- Combating Label Distribution Shift for Active Domain Adaptation
- Authors
- Hwang, Sehyun; Lee, Sohyun; Kim, Sungyeon; Ok, Jungseul; Kwak, Suha
- Date Issued
- 2022-10-23
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Abstract
- We consider the problem of active domain adaptation (ADA) to unlabeled target data, of which subset is actively selected and labeled given a budget constraint. Inspired by recent analysis on a critical issue from label distribution mismatch between source and target in domain adaptation, we devise a method that addresses the issue for the first time in ADA. At its heart lies a novel sampling strategy, which seeks target data that best approximate the entire target distribution as well as being representative, diverse, and uncertain. The sampled target data are then used not only for supervised learning but also for matching label distributions of source and target domains, leading to remarkable performance improvement. On four public benchmarks, our method substantially outperforms existing methods in every adaptation scenario.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/114633
- Article Type
- Conference
- Citation
- 17th European Conference on Computer Vision, ECCV 2022, page. 549 - 566, 2022-10-23
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