Masking: A New Perspective of Noisy Supervision,
[code].
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels,
[code].
How does Disagreement Help Generalization against Label Corruption,
[code].
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations,
[code].
Towards Robust ResNet: A Small Step but A Giant Leap,
[code].
Are Anchor Points Really Indispensable in Label-noise Learning,
[code].
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust,
[code].
Variational Imitation Learning from Diverse-quality Demonstrations,
[code].
Friendly Adversarial Training,
[code].
Searching to Exploit Memorization Effect in Learning from Noisy Labels,
[code].
Learning with Multiple Complementary Labels,
[code].
Provably Consistent Partial-Label Learning,
[code].
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning, [code].
Part-dependent Label Noise: Towards Instance-dependent Label Noise,
[code].
Learning with Group Noise,
[code].
Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model,
[code].
Geometry-aware Instance-reweighted Adversarial Training,
[code].
Robust Early-learning: Hindering the Memorization of Noisy Labels,
[code].
Confidence Scores Make Instance-dependent Label-noise Learning Possible,
[code].
Maximum Mean Discrepancy is Aware of Adversarial Attacks,
[code].
Learning Diverse-Structured Networks for Adversarial Robustness,
[code].
Class2Simi: A Noise Reduction Perspective on Learning with Noisy Labels,
[code].
Provably End-to-end Label-noise Learning without Anchor Points,
[code].
Probabilistic Margins for Instance Reweighting in Adversarial Training,
[code].
Instance-dependent Label-noise Learning under a Structural Causal Model,
[code].
Adversarial Robustness Through the Lens of Causality, [code].
Exploiting Class Activation Value for Partial-Label Learning, [code].
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability, [code].
Reliable Adversarial Distillation with Unreliable Teachers, [code].
Rethinking Class-Prior Estimation for Positive-Unlabeled Learning, [code].
Sample Selection with Uncertainty of Losses for Learning with Noisy Labels, [code].
Contrastive Learning with Boosted Memorization, [code].
Fast and Reliable Evaluation of Adversarial Robustness, [code].
Virtual Homogeneity Learning, [code].
Modeling Adversarial Noise for Adversarial Defense, [code].
Improving Adversarial Robustness via Natural and Adversarial Mutual Information, [code].
Understanding Robust Overfitting of Adversarial Training and Beyond, [code].
Estimating Instance-dependent Label-noise Transition Matrix using DNNs, [code].
NoiLin: Improving Adversarial Training and Correcting Stereotype of Noisy Labels, [code].
Fair Classification with Instance-dependent Label Noise, [code].