Year: 2026
Type: article
Abstract: Addressing the challenge of noisy labels in large datasets, this paper introduces a novel domain adaptation method leveraging comparative distillation model training. By transferring knowledge from a source domain model to a target domain, we construct a class correlation matrix and obtain a noise transfer matrix through network training. This matrix is used to correct noisy labels, enhancing the model’s generalization and robustness. The singular value decomposition of the noise transfer matrix constrains its training, while Tsallis entropy smooths the model’s output probabilities. Experimental results on the Office-31 and Office-Home datasets demonstrate significant improvements in classification accuracy, showcasing the effectiveness of our approach.
Source: Scientific Reports
Authors: Yapei Feng, Junhui Liu, Hua Zhong
Institution: Hangzhou Dianzi University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Award: G4019873335
OpenAlex ID: W7127976850