loss correction

Corruptions of Supervised Learning Problems: Typology and Mitigations JMLR 2026 FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels WACV 2026 Regroup Median Loss for Combating Label Noise AAAI 2024 Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction ACL 2022 Multi Label Loss Correction against Missing and Corrupted Labels ACML 2022 Trusted Loss Correction for Noisy Multi-Label Learning ACML 2022 Breaking the Dilemma of Medical Image-to-image Translation NeurIPS 2021 Learn with Noisy Data via Unsupervised Loss Correction for Weakly Supervised Reading Comprehension COLING 2020 Training Noise-Robust Deep Neural Networks via Meta-Learning CVPR 2020 Unsupervised Label Noise Modeling and Loss Correction ICML 2019 Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise NeurIPS 2018 Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach CVPR 2017