Computer Vision

PARS: Pseudo-Label Aware Robust Sample Selection for Learning with Noisy Labels
We propose a novel pseudo-label aware robust sample selection method for learning with noisy labels that outperforms state-of-the-art especially in presence of high label noise.
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