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CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning
[article]
2021
arXiv
pre-print
Labels are costly and sometimes unreliable. Noisy label learning, semi-supervised learning, and contrastive learning are three different strategies for designing learning processes requiring less annotation cost. Semi-supervised learning and contrastive learning have been recently demonstrated to improve learning strategies that address datasets with noisy labels. Still, the inner connections between these fields as well as the potential to combine their strengths together have only started to
arXiv:2111.11652v1
fatcat:4qfk4z4eazbxviswhwjb5lvawy