Noisy Label
Noisy label learning (NLL) tackles the challenge of training machine learning models on datasets containing inaccurate labels, a common problem in large-scale data collection. Current research focuses on developing robust algorithms and model architectures, such as vision transformers and graph neural networks, that can effectively mitigate the negative impact of noisy labels, often employing techniques like sample selection, loss function modification, and self-supervised learning. These advancements are crucial for improving the reliability and generalizability of machine learning models across various applications, from image classification and natural language processing to medical image analysis and remote sensing. The ultimate goal is to build more robust and reliable AI systems that can handle the imperfections inherent in real-world data.
Papers
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise
Bidur Khanal, Tianhong Dai, Binod Bhattarai, Cristian Linte
Graph Anomaly Detection with Noisy Labels by Reinforcement Learning
Zhu Wang, Shuang Zhou, Junnan Dong, Chang Yang, Xiao Huang, Shengjie Zhao
Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation
Louis L. Chen, Bobbie Chern, Eric Eckstrand, Amogh Mahapatra, Johannes O. Royset
Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection
Suyeon Kim, Dongha Lee, SeongKu Kang, Sukang Chae, Sanghwan Jang, Hwanjo Yu
Relation Modeling and Distillation for Learning with Noisy Labels
Xiaming Che, Junlin Zhang, Zhuang Qi, Xin Qi