15 citations · 29 across the 10 of their papers we have counts for
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cs.CV2023★ 15 cited
Source-Free Unsupervised Domain Adaptation: A Survey
Yuqi Fang, Pew-Thian Yap, Weili Lin +2
Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention for tackling domain-shift problems caused by distribution discrepancy across different doma…
cs.CV2022★ 1 cited
ActMAD: Activation Matching to Align Distributions for Test-Time-Training
Muhammad Jehanzeb Mirza, Pol Jané Soneira, Wei Lin +3
Test-Time-Training (TTT) is an approach to cope with out-of-distribution (OOD) data by adapting a trained model to distribution shifts occurring at test-time. We propose to perform…