Active Learning with Foundation Model Priors: Efficient Learning under Class Imbalance

arXiv:2606.07630v1 Announce Type: new Abstract: Real-world datasets across image and text domains are often characterized by skewed class distributions and noisy annotations, which jointly degrade model performance, particularly on minority classes. Among existing solutions, active learning offers an effective and efficient paradigm by selectively querying the most informative and balanced samples for annotation. We propose an innovative active learning framework that mitigates class imbalance and selects the most informative samples to annotate. Leveraging foundation model priors, our algorit

Source: arXiv cs.LG — read the full report at the original publisher.

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