label noise

Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes NeurIPS 2024 Structured Probabilistic Coding AAAI 2024 Suppressing Uncertainty in Gaze Estimation AAAI 2024 SURE: SUrvey REcipes for building reliable and robust deep networks CVPR 2024 Toward Robustness in Multi-Label Classification: A Data Augmentation Strategy against Imbalance and Noise AAAI 2024 Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning ACL 2024 Towards Robust Multi-Label Learning against Dirty Label Noise IJCAI 2024 Trusted Multi-view Learning with Label Noise IJCAI 2024 Trusted re-weighting for label distribution learning UAI 2024 Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks ICLR 2024 Understanding Domain Generalization: A Noise Robustness Perspective ICLR 2024 Unlocking the Power of Open Set: A New Perspective for Open-Set Noisy Label Learning AAAI 2024 Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models ICLR 2024 VEIL: Vetting Extracted Image Labels from In-the-Wild Captions for Weakly-Supervised Object Detection EACL 2024 VF-Detector: Making Multi-Granularity Code Changes on Vulnerability Fix Detector Robust to Mislabeled Changes IJCAI 2024 What Makes Partial-Label Learning Algorithms Effective? NeurIPS 2024 Adaptive Sample Selection for Robust Learning Under Label Noise WACV 2023 Adaptive Textual Label Noise Learning based on Pre-trained Models EMNLP 2023 A Holistic View of Label Noise Transition Matrix in Deep Learning and Beyond ICLR 2023 A law of adversarial risk, interpolation, and label noise ICLR 2023 ALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning NeurIPS 2023 A Path to Simpler Models Starts With Noise NeurIPS 2023 AQuA: A Benchmarking Tool for Label Quality Assessment NeurIPS 2023 A Theory of Dynamic Benchmarks ICLR 2023 Automated Classification of Model Errors on ImageNet NeurIPS 2023