Work

CLIP-Guided Adaptive Sample Selection with Machine Unlearning and Negative Learning Strategies for Noisy Labels Learning

Year: 2026

Type: article

Abstract: Deep neural networks (DNNs), due to their over-parameterization and memorization capacity, are prone to fitting incorrect labels in training data, which can substantially degrade their performance. Consequently, LNL has attracted increasing attention. Popular LNL methods select potentially clean samples for reliable supervision while applying robust techniques to the remaining samples to mitigate the adverse effects of label noise. However, when sample selection relies solely on the continuously updated classifier, the classifier may gradually memorize incorrect labels during training, and the resulting biased predictions can further affect subsequent sample selection, leading to a self-reinforcing confirmation bias. To address this issue, we propose CAMN, a CLIP-guided adaptive sample selection with machine unlearning and negative learning strategies for noisy labels learning. Specifically, CAMN introduces a pretrained vision language model CLIP as an external semantic prior independent of the task-specific classifier to perform preliminary noise-aware sample selection. The resulting selections are then integrated with an adaptive and balanced sample selection strategy to provide more reliable samples for robust learning. CLIP provides an independent external semantic prior for reducing classifier-induced bias, while the selection results from the trained DNN complements CLIP-based zero-shot selection by reducing over-reliance on its predictions and promoting more balanced sample selection. After selection, CAMN further incorporates a machine unlearning mechanism and a negative learning strategy as regularizers, reducing the persistent influence of noisy labels on DNN and further enhancing its robustness. Experiments demonstrate the effectiveness and robustness of CAMN across diverse noise settings, including synthetic and Web-crawled noisy datasets.

Source: Symmetry

DOI: 10.3390/sym18101691

Authors: Qian Zhang, Yanzhan Chen, Yongqi Yuan, Ge Jin, Yong Cheng

Institution: Nanjing Forestry University

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China