label noise

Do We Really Need Gold Samples for Sample Weighting Under Label Noise? WACV 2021 DualGraph: A Graph-Based Method for Reasoning About Label Noise CVPR 2021 Efficient Learning with Arbitrary Covariate Shift ALT 2021 Exploring Inspiration Sets in a Data Programming Pipeline for Product Moderation ACL 2021 Faster Meta Update Strategy for Noise-Robust Deep Learning CVPR 2021 FINE Samples for Learning with Noisy Labels NeurIPS 2021 Generalized DataWeighting via Class-Level Gradient Manipulation NeurIPS 2021 Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization ICLR 2021 Hierarchical Modeling of Label Dependency and Label Noise in Fine-grained Entity Typing IJCAI 2021 How Benign is Benign Overfitting ? ICLR 2021 How does a Neural Network's Architecture Impact its Robustness to Noisy Labels? NeurIPS 2021 Improved Regularization and Robustness for Fine-tuning in Neural Networks NeurIPS 2021 Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising Learning EMNLP 2021 Improving Label Noise Robustness with Data Augmentation and Semi-Supervised Learning (Student Abstract) AAAI 2021 Instance-adaptive training with noise-robust losses against noisy labels EMNLP 2021 Instance-dependent Label-noise Learning under a Structural Causal Model NeurIPS 2021 Interactive Label Cleaning with Example-based Explanations NeurIPS 2021 Knowledge Distillation with Noisy Labels for Natural Language Understanding EMNLP 2021 Label Noise SGD Provably Prefers Flat Global Minimizers NeurIPS 2021 Learning Deep Neural Networks under Agnostic Corrupted Supervision ICML 2021 Learning from Crowds by Modeling Common Confusions AAAI 2021 Learning From Noisy Data With Robust Representation Learning ICCV 2021 Learning from Noisy Labels with Complementary Loss Functions AAAI 2021 Learning from Noisy Labels with No Change to the Training Process ICML 2021 Learning Neural Network Subspaces ICML 2021