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Adapting to Distribution Shift by Visual Domain Prompt Generation
May 7, 2024, 4:43 a.m. | Zhixiang Chi, Li Gu, Tao Zhong, Huan Liu, Yuanhao Yu, Konstantinos N Plataniotis, Yang Wang
cs.LG updates on arXiv.org arxiv.org
Abstract: In this paper, we aim to adapt a model at test-time using a few unlabeled data to address distribution shifts. To tackle the challenges of extracting domain knowledge from a limited amount of data, it is crucial to utilize correlated information from pre-trained backbones and source domains. Previous studies fail to utilize recent foundation models with strong out-of-distribution generalization. Additionally, domain-centric designs are not flavored in their works. Furthermore, they employ the process of modelling …
abstract adapt aim arxiv challenges cs.cv cs.lg data distribution domain domain knowledge information knowledge paper prompt shift test type visual
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