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FGCM: Noisy Label Learning via Fine-Grained Confidence Modeling
A small portion of mislabeled data can easily limit the performance of deep neural networks (DNNs) due to their high capacity for memorizing random labels. Thus, robust learning from noisy labels has become a key challenge for deep learning due to inadequate datasets with high-quality annotations. Most existing methods involve training models on clean sets by dividing clean samples from noisy ones, resulting in large amounts of mislabeled data being unused. To address this problem, we proposedoi:10.3390/app122211406 fatcat:kjnpzsl7x5ckfo32ewril7orpe