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cs.LG2026
Learning Fair Domain Adaptation with Virtual Label Distribution
Yuguang Zhang, Lijun Sheng, Jian Liang +1
Unsupervised Domain Adaptation (UDA) aims to mitigate performance degradation when training and testing data are sampled from different distributions. While significant progress ha…
cs.LG2024
STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory Replay
Yongcan Yu, Lijun Sheng, Ran He +1
Test-time adaptation (TTA) aims to address the distribution shift between the training and test data with only unlabeled data at test time. Existing TTA methods often focus on impr…
cs.LG2023
Unleashing the power of Neural Collapse for Transferability Estimation
Yuhe Ding, Bo Jiang, Lijun Sheng +2
Transferability estimation aims to provide heuristics for quantifying how suitable a pre-trained model is for a specific downstream task, without fine-tuning them all. Prior studie…