3 papers
cs.LG2026
Understanding and Correcting Low-Frequency Bias in EEG Foundation Model
Junjie Yu, Zihan Deng, Jianyu Zhang +8
Increasing EEG pretraining data scale or model capacity does not consistently improve downstream performance. We identify a persistent low-frequency bias in representations learned…
cs.LG2026
Pretraining Induces a Reusable Spectral Basis for Downstream Task Adaptation
Junjie Yu, Yue Wang, Zihan Deng +3
Finetuning pretrained models occurs in a low-dimensional subspace of the full parameter space. Prior work has focused on characterizing this optimization subspace, but largely igno…
cs.CV2023
Enhancing Few-shot CLIP with Semantic-Aware Fine-Tuning
Yao Zhu, Yuefeng Chen, Wei Wang +7
Learning generalized representations from limited training samples is crucial for applying deep neural networks in low-resource scenarios. Recently, methods based on Contrastive La…