gohsyi / PeerLoss
Learning with Noisy Labels by adopting a peer prediction loss function.
☆35Updated 5 years ago
Alternatives and similar repositories for PeerLoss:
Users that are interested in PeerLoss are comparing it to the libraries listed below
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆37Updated 4 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆34Updated 4 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆84Updated 5 years ago
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 6 years ago
- ICML'19: How does Disagreement Help Generalization against Label Corruption?☆21Updated 5 years ago
- "Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness" (NeurIPS 2020).☆50Updated 4 years ago
- Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in fu…☆53Updated 3 years ago
- NeurIPS'18: Masking: A New Perspective of Noisy Supervision☆54Updated 6 years ago
- Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)☆100Updated 3 years ago
- Code for ICLR 2019 Paper, "MAX-MIG: AN INFORMATION THEORETIC APPROACH FOR JOINT LEARNING FROM CROWDS"☆25Updated last year
- Self-supervised Label Augmentation via Input Transformations (ICML 2020)☆106Updated 4 years ago
- Code release for Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML2019)☆81Updated 5 years ago
- ☆28Updated 3 years ago
- [NeurIPS 2017] [ICML 2019] Code for complementary-label learning☆48Updated last year
- [ICML 2021] “ Self-Damaging Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Bobak Mortazavi, Zhangyang Wang☆63Updated 3 years ago
- Example implementation for the paper: (ICLR Oral) Learning Robust Representations by Projecting Superficial Statistics Out☆27Updated 4 years ago
- MoPro: Webly Supervised Learning☆87Updated this week
- Description Code for the paper "Robust Inference via Generative Classifiers for Handling Noisy Labels".☆32Updated 5 years ago
- Code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning", Ren et al., NeurIPS'20☆25Updated 4 years ago
- Code release for Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning (NeurIPS 2019)☆24Updated 3 years ago
- Meta-Learning based Noise-Tolerant Training☆124Updated 4 years ago
- Official Caffe implementation of Boosting Domain Adaptation by Discovering Latent Domains.☆23Updated 4 years ago
- Code for the article "Confidence Scores Make Instance-dependent Label-noise Learning Possible", ICML'21☆9Updated 8 months ago
- [NeurIPS 2020] “ Robust Pre-Training by Adversarial Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang☆114Updated 3 years ago
- This is the code of CVPR'20 paper "Distilling Cross-Task Knowledge via Relationship Matching".☆49Updated 4 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated last year
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- Improving generalization by controlling label-noise information in neural network weights.☆40Updated 4 years ago
- Meta Label Correction for Noisy Label Learning☆85Updated 2 years ago
- Official PyTorch implementation of "Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity" (ICLR'21 Oral)☆103Updated 3 years ago