Collaborative Cross Learning reduces Semantic Contamination in noisy-label training by aligning embeddings across views and models, improving accuracy on CIFAR and real-world noisy datasets.
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Combating Semantic Contamination in Learning with Label Noise
Collaborative Cross Learning reduces Semantic Contamination in noisy-label training by aligning embeddings across views and models, improving accuracy on CIFAR and real-world noisy datasets.