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cs.LG2025
An Inclusive Theoretical Framework of Robust Supervised Contrastive Loss against Label Noise
Jingyi Cui, Yi-Ge Zhang, Hengyu Liu +1
Learning from noisy labels is a critical challenge in machine learning, with vast implications for numerous real-world scenarios. While supervised contrastive learning has recently…
cs.LG2025
Difficult Examples Hurt Unsupervised Contrastive Learning: A Theoretical Perspective
Yi-Ge Zhang, Jingyi Cui, Qiran Li +1
Unsupervised contrastive learning has shown significant performance improvements in recent years, often approaching or even rivaling supervised learning in various tasks. However,…