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Learning with Imbalanced Noisy Data by Preventing Bias in Sample Selection
Specifically, we propose Class-Balance-based sample Selection (CBS) to prevent the tail class samples from being neglected during training.
Adaptive Integration of Partial Label Learning and Negative Learning for Enhanced Noisy Label Learning
There has been significant attention devoted to the effectiveness of various domains, such as semi-supervised learning, contrastive learning, and meta-learning, in enhancing the performance of methods for noisy label learning (NLL) tasks.
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