Bo Han


Codes and Data (Reproducible Research)

  • 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].

  • Attacks Which Do Not Kill Training Make Adversarial Learning Stronger, [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].

  • TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation, [code].

  • Instance-dependent Label-noise Learning under a Structural Causal Model, [code].

  • Understanding and Improving Early Stopping for Learning with Noisy Labels, [code].