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Found 2 papers, 1 papers with code
Fine-Grained Classification with Noisy Labels
To this end, we propose a novel framework called stochastic noise-tolerated supervised contrastive learning (SNSCL) that confronts label noise by encouraging distinguishable representation.
Learning to Rectify for Robust Learning with Noisy Labels
In this paper, we propose warped probabilistic inference (WarPI) to achieve adaptively rectifying the training procedure for the classification network within the meta-learning scenario.
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