{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/training-noise-robust-deep-neural-networks","title":"Training Noise-Robust Deep Neural Networks via Meta-Learning","arxiv_id":null,"date":"2020-06-01","proceeding":"CVPR 2020 6","authors":["Zhen Wang"," Guosheng Hu"," Qinghua Hu"],"abstract":"Label noise may significantly degrade the performance of Deep Neural Networks (DNNs). To train noise-robust DNNs, Loss correction (LC) approaches have been introduced. LC approaches assume the noisy labels are corrupted from clean (ground-truth) labels by an unknown noise transition matrix T. The backbone DNNs and T can be trained separately, where T is approximated with prior knowledge. For example, T is constructed by stacking the maximum or mean predic- tions of the samples from each class. In this work, we pro- pose a new loss correction approach, named as Meta Loss Correction (MLC), to directly learn T from data via the meta-learning framework. The MLC is model-agnostic and learns T from data rather than heuristically approximates it using prior knowledge. Extensive evaluations are conducted on computer vision (MNIST, CIFAR-10, CIFAR-100, Cloth- ing1M) and natural language processing (Twitter) datasets. The experimental results show that MLC achieves very com- petitive performance against state-of-the-art approaches.\r","url_abs":"http://openaccess.thecvf.com/content_CVPR_2020/html/Wang_Training_Noise-Robust_Deep_Neural_Networks_via_Meta-Learning_CVPR_2020_paper.html","url_pdf":"http://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Training_Noise-Robust_Deep_Neural_Networks_via_Meta-Learning_CVPR_2020_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"training-noise-robust-deep-neural-networks","repo_url":"https://github.com/ZhenWang-PhD/Training-Noise-Robust-Deep-Neural-Networks-via-Meta-Learning","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"meta-learning","task_name":"Meta-Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}