bhanML / coteaching_plus
ICML'19: How does Disagreement Help Generalization against Label Corruption?
☆21Updated 5 years ago
Alternatives and similar repositories for coteaching_plus:
Users that are interested in coteaching_plus are comparing it to the libraries listed below
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 6 years ago
- ☆9Updated last year
- ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels☆91Updated 4 years ago
- ☆13Updated 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
- Description Code for the paper "Robust Inference via Generative Classifiers for Handling Noisy Labels".☆32Updated 5 years ago
- Code for "Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers"☆27Updated 3 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆84Updated 5 years ago
- Meta-Learning based Noise-Tolerant Training☆124Updated 4 years ago
- Self-Paced Multi-view Co-training for person re-id experiment☆30Updated 3 years ago
- Code for NeurIPS 2019 Paper, "L_DMI: An Information-theoretic Noise-robust Loss Function"☆119Updated last year
- [NeurIPS 2017] [ICML 2019] Code for complementary-label learning☆48Updated last year
- Code for ICML2020 "Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation"☆90Updated 4 years ago
- Code for the paper "Generalizing to Unseen Domains via Adversarial Data Augmentation", NeurIPS 2018☆121Updated 5 years ago
- Implementation and datasets for Efficient Domain Generalization via Common-Specific Low-Rank Decomposition (https://arxiv.org/abs/2003.12…☆52Updated 4 years ago
- Code for the paper: On Symmetric Losses for Learning from Corrupted Labels☆19Updated 5 years ago
- PyTorch implementation of Weighted Batch-Normalization layers☆37Updated 4 years ago
- Code release for Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers (ICML2019)☆81Updated 5 years ago
- ☆34Updated 7 years ago
- Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆80Updated 4 years ago
- TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER☆118Updated 8 years ago
- Example implementation for the paper: (ICLR Oral) Learning Robust Representations by Projecting Superficial Statistics Out☆27Updated 4 years ago
- NeurIPS 2019 : Learning to Propagate for Graph Meta-Learning☆36Updated 5 years ago
- Code release for Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning (NeurIPS 2019)☆24Updated 3 years ago
- Official repository for Reliable Label Bootstrapping☆19Updated 2 years ago
- Learning with Noisy Labels by adopting a peer prediction loss function.☆35Updated 5 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆43Updated 2 years ago
- Official Caffe implementation of Boosting Domain Adaptation by Discovering Latent Domains.☆23Updated 4 years ago
- A PyTorch implementation for Asymmetric Tri-training for Unsupervised Domain Adaptation☆44Updated 7 years ago