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Channel-Wise Contrastive Learning for Learning with Noisy Labels
In real-world datasets, noisy labels are pervasive.
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels
In recent years, research on learning with noisy labels has focused on devising novel algorithms that can achieve robustness to noisy training labels while generalizing to clean data.
Bio-Inspired Feature Selection in Brain Disease Detection via an Improved Sparrow Search Algorithm
Finally, the proposed ISSA is utilized to solve the objective function.
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels
The early stopping strategy averts updating CNNs during the early training phase and is widely employed in the presence of noisy labels.
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