UASPL: Uncertainty-Aware Self-Paced Learning with Evidential Neural Networks

2026-07-07

AI Summary

This work addresses a key limitation of traditional self-paced learning (SPL), which relies solely on loss values to distinguish easy from hard samples and often misclassifies unreliable samples as “easy,” thereby compromising training stability. To overcome this issue, the study introduces uncertainty estimation into the SPL framework for the first time, proposing an uncertainty-aware SPL method grounded in evidential deep learning and subjective logic. By incorporating prediction reliability into the sample selection process through an interpretable and scalable general loss function, the approach enables robust curriculum learning that progresses from easy to hard samples. Extensive experiments demonstrate that the proposed method significantly outperforms existing SPL techniques across multiple datasets, achieving superior performance in classification accuracy, generalization capability, and model interpretability.

Abstract

Self-paced learning (SPL) is an effective learning paradigm that simulates the human learning process by progressing from easy to difficult samples based on the value of the loss function during the learning process. It has shown great potential in improving model performance and training efficiency. However, the prediction results of samples with smaller loss values are not necessarily reliable, indicating that such samples are not always simple samples for the model. Hence, this article proposes an uncertainty-aware self-paced learning based on evidential neural networks, termed UASPL, which integrates predictive reliability into sample selection through a general loss function within the Subjective Logic framework. This loss function incorporates uncertainty estimation and can be extended to different variants of SPL. Moreover, this loss function couples a sample selection preference, thereby ensuring the interpretability of the sample selection process. Finally, the experimental results on multiple datasets show that UASPL outperforms other SPL methods in terms of classification performance, interpretability, and generality. The source code is available at: https://github.com/treelife979/UASPL.

Problem

• Self-paced learning
• Uncertainty estimation
• Evidential neural networks
• Sample selection
• Predictive reliability

Innovation

• Uncertainty-Aware Learning
• Self-Paced Learning
• Evidential Neural Networks
• Subjective Logic
• Sample Selection