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Abstract: Real-world datasets often suffer from both noisy labels and imbalanced class distribution, presenting significant challenges for the effective deployment of deep neural networks (DNNs).

These approaches, however, can lead to redundancy in labels and inherit the biases associated with the imbalance in datasets. To minimize sample redundancy and alleviate data imbalance, we propose a ...