5 citations · 22 across the 16 of their papers we have counts for
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cs.CV2022★ 2 cited
Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes
Sungha Choi, Seunghan Yang, Seokeon Choi +1
This paper proposes a novel test-time adaptation strategy that adjusts the model pre-trained on the source domain using only unlabeled online data from the target domain to allevia…
cs.SD2022
Domain Agnostic Few-shot Learning for Speaker Verification
Seunghan Yang, Debasmit Das, Janghoon Cho +2
Deep learning models for verification systems often fail to generalize to new users and new environments, even though they learn highly discriminative features. To address this pro…