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cs.LG2025
Reliable Active Learning from Unreliable Labels via Neural Collapse Geometry
Atharv Goel, Sharat Agarwal, Saket Anand +1
Active Learning (AL) promises to reduce annotation cost by prioritizing informative samples, yet its reliability is undermined when labels are noisy or when the data distribution s…
cs.LG2025
Prototype-Guided Pseudo-Labeling with Neighborhood-Aware Consistency for Unsupervised Adaptation
Eman Ali, Chetan Arora, Muhammad Haris Khan
In unsupervised adaptation for vision-language models such as CLIP, pseudo-labels derived from zero-shot predictions often exhibit significant noise, particularly under domain shif…