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cs.CV2026
Closing the Confusion Loop: CLIP-Guided Alignment for Source-Free Domain Adaptation
Shanshan Wang, Ziying Feng, Xiaozheng Shen +4
Source-Free Domain Adaptation (SFDA) tackles the problem of adapting a pre-trained source model to an unlabeled target domain without accessing any source data, which is quite suit…
cs.CV2024
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation
Shanshan Wang, Hao Zhou, Xun Yang +4
Unsupervised domain adaptation (UDA) is a critical problem for transfer learning, which aims to transfer the semantic information from labeled source domain to unlabeled target dom…