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The high capacity of deep learning models to learn complex patterns poses asignificant challenge when confronted with label noise. The inability todifferentiate clean and noisy labels ultimately results in poor generalization.We approach this problem by reassigning the label for each image using a newteacher-student based framework termed P-LC (pseudo-label correction).Traditional teacher-student networks are composed of teacher and studentclassifiers for knowledge distillation. In our novel approach, we reconfigurethe teacher network into a triple encoder, leveraging the triplet loss toestablish a pseudo-label correction system. As the student generates pseudolabels for a set of given images, the teacher learns to choose between theinitially assigned labels and the pseudo labels. Experiments on MNIST,Fashion-MNIST, and SVHN demonstrate P-LC's superior performance over existingstate-of-the-art methods across all noise levels, most notably in high noise.In addition, we introduce a noise level estimation to help assess modelperformance and inform the need for additional data cleaning procedures.

Pseudo-label Correction for Instance-dependent Noise Using Teacher-student Framework