Learning to Purify Noisy Labels via Meta Soft Label Corrector


  • Yichen Wu Xi'an Jiaotong University, Shaanxi, China
  • Jun Shu Xi'an Jiaotong University, Shaanxi, China
  • Qi Xie Xi'an Jiaotong University, Shaanxi, China
  • Qian Zhao Xi'an Jiaotong University, Shaanxi, China
  • Deyu Meng Xi'an Jiaotong University, Shaanxi, China Pazhou Lab, Guangzhou, 510330, China Macau University of Science and Technology, Macau, China




Transfer/Adaptation/Multi-task/Meta/Automated Learning


Recent deep neural networks (DNNs) can easily overfit to biased training data with noisy labels. Label correction strategy is commonly used to alleviate this issue by identifying suspected noisy labels and then correcting them. Current approaches to correcting corrupted labels usually need manually pre-defined label correction rules, which makes it hard to apply in practice due to the large variations of such manual strategies with respect to different problems. To address this issue, we propose a meta-learning model, aiming at attaining an automatic scheme which can estimate soft labels through meta-gradient descent step under the guidance of a small amount of noise-free meta data. By viewing the label correction procedure as a meta-process and using a meta-learner to automatically correct labels, our method can adaptively obtain rectified soft labels gradually in iteration according to current training problems. Besides, our method is model-agnostic and can be combined with any other existing classification models with ease to make it available to noisy label cases. Comprehensive experiments substantiate the superiority of our method in both synthetic and real-world problems with noisy labels compared with current state-of-the-art label correction strategies.




How to Cite

Wu, Y., Shu, J., Xie, Q., Zhao, Q., & Meng, D. (2021). Learning to Purify Noisy Labels via Meta Soft Label Corrector. Proceedings of the AAAI Conference on Artificial Intelligence, 35(12), 10388-10396. https://doi.org/10.1609/aaai.v35i12.17244



AAAI Technical Track on Machine Learning V