  Learning with noisy labels can significantly hinder the generalization
performance of deep neural networks (DNNs). Existing approaches address this
issue through loss correction or example selection methods. However, these
methods often rely on the model's predictions obtained from the softmax
function, which can be over-confident and unreliable. In this study, we
identify the translation invariance of the softmax function as the underlying
cause of this problem and propose the \textit{Dirichlet-based Prediction
Calibration} (DPC) method as a solution. Our method introduces a calibrated
softmax function that breaks the translation invariance by incorporating a
suitable constant in the exponent term, enabling more reliable model
predictions. To ensure stable model training, we leverage a Dirichlet
distribution to assign probabilities to predicted labels and introduce a novel
evidence deep learning (EDL) loss. The proposed loss function encourages
positive and sufficiently large logits for the given label, while penalizing
negative and small logits for other labels, leading to more distinct logits and
facilitating better example selection based on a large-margin criterion.
Through extensive experiments on diverse benchmark datasets, we demonstrate
that DPC achieves state-of-the-art performance. The code is available at
https://github.com/chenchenzong/DPC.
