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DEFT: Disentanglement-Enhanced Fine-Tuning for EEG Foundation Models
Yuqiu Deng, Muyun Jiang, Zongpeng Zhang +8
EEG foundation models (EFMs) have improved EEG representation learning by transferring knowledge from large-scale multi-subject pretraining to downstream tasks. However, their pret…
Temporal Restoration and Spatial Rewiring for Source-Free Multivariate Time Series Domain Adaptation
Peiliang Gong, Yucheng Wang, Min Wu +3
Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained model from an annotated source domain to an unlabelled target domain without accessing the source data, thereby pre…
Bridging Distribution Gaps in Time Series Foundation Model Pretraining with Prototype-Guided Normalization
Peiliang Gong, Emadeldeen Eldele, Min Wu +3
Foundation models have achieved remarkable success across diverse machine-learning domains through large-scale pretraining on large, diverse datasets. However, pretraining on such…
Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation
Peiliang Gong, Mohamed Ragab, Min Wu +4
Test-time adaptation aims to adapt pre-trained deep neural networks using solely online unlabelled test data during inference. Although TTA has shown promise in visual applications…
Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation
Yucheng Wang, Peiliang Gong, Min Wu +4
Time-Series (TS) data has grown in importance with the rise of Internet of Things devices like sensors, but its labeling remains costly and complex. While Unsupervised Domain Adapt…