5 citations · 8 across the 2 of their papers we have counts for
2 papers
cs.CV2024★ 3 cited
SF(DA): Source-free Domain Adaptation Through the Lens of Data Augmentation
Uiwon Hwang, Jonghyun Lee, Juhyeon Shin +1
In the face of the deep learning model's vulnerability to domain shift, source-free domain adaptation (SFDA) methods have been proposed to adapt models to new, unseen target domain…
cs.CV2024★ 5 cited
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
Jonghyun Lee, Dahuin Jung, Saehyung Lee +4
Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online…