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Instance-based Label Smoothing For Better Calibrated Classification Networks
[article]
2021
arXiv
pre-print
Label smoothing is widely used in deep neural networks for multi-class classification. While it enhances model generalization and reduces overconfidence by aiming to lower the probability for the predicted class, it distorts the predicted probabilities of other classes resulting in poor class-wise calibration. Another method for enhancing model generalization is self-distillation where the predictions of a teacher network trained with one-hot labels are used as the target for training a student
arXiv:2110.05355v1
fatcat:mzzw6msoprf4vpc2phc2fyeqrq