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cs.LG2026
Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance
Jiancheng Zhang, Meiqing Li, Qi Zhang +1
Real-world datasets across image and text domains are often characterized by skewed class distributions and noisy annotations, which jointly degrade model performance, particularly…
cs.LG2025
Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data
Jiancheng Zhang, Yinglun Zhu
Active learning (AL) is a principled strategy to reduce annotation cost in data-hungry deep learning. However, existing AL algorithms focus almost exclusively on unimodal data, ove…
cs.LG2025
Mixtraining: A Better Trade-Off Between Compute and Performance
Zexin Li, Jiancheng Zhang, Yufei Li +2
Incorporating self-supervised learning (SSL) before standard supervised learning (SL) has become a widely used strategy to enhance model performance, particularly in data-limited s…