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1. C2MT C2MT Public

   This is the official code for C2MT: Cross-to-merge training with class balance strategy for learning with noisy labels

   Python 4

2. BPT-PLR BPT-PLR Public

   This is the official code for Balanced Partitioning and Training Framework with Pseudo-Label Relaxed Contrastive Loss for Noisy Label Learning. It will be available when the paper is acctepted.

   Python 2

3. JNPL JNPL Public

   Forked from Mars-Wei/JNPL

   the implementation for the paper(Joint Negative and Positive Learning for Noisy Labels)

   Python 1

4. SANM SANM Public

   Python 1

5. Learning-with-noisy-labels-revisited-A-study-using-real-world-human-annotations. Learning-with-noisy-labels-revisited-A-study-using-real-world-human-annotations. Public

   Forked from UCSC-REAL/cifar-10-100n

   Human annotated noisy labels for CIFAR-10 and CIFAR-100. The website of CIFAR-N is available at http://www.noisylabels.com/.

   Python 1

6. PSSCL PSSCL Public

   This is the official code for PSSCL: A Progressive Sample Selection strategy with Contrastive Loss for Learning with Noisy Labels

   Python 1