takashiishida / compLinks
[NeurIPS 2017] [ICML 2019] Code for complementary-label learning
☆49Updated last month
Alternatives and similar repositories for comp
Users that are interested in comp are comparing it to the libraries listed below
Sorting:
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆127Updated last year
- Code for 'Joint Optimization Framework for Learning with Noisy Labels'☆38Updated 7 years ago
- Implementation and datasets for Efficient Domain Generalization via Common-Specific Low-Rank Decomposition (https://arxiv.org/abs/2003.12…☆53Updated 5 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆43Updated 2 years ago
- Code for CVPR2020 ‘Training Noise Robust Deep Neural Networks via Meta-Learning’☆20Updated 5 years ago
- [AAAI 2021] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning☆139Updated 4 years ago
- Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization☆128Updated 5 months ago
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆79Updated 5 years ago
- SwAV for CIFAR-10, adapted from https://github.com/facebookresearch/swav☆29Updated 3 years ago
- ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels☆78Updated 4 years ago
- A pytorch implementation for "Neighborhood Collective Estimation for Noisy Label Identification and Correction", which is accepted by ECC…☆26Updated 2 years ago
- Feature-Critic Networks for Heterogeneous Domain Generalisation☆53Updated 6 years ago
- Code for our ECCV paper -- "Learning to Balance Specificity and Invariance for In and Out of Domain Generalization"☆56Updated 5 years ago
- Code release for Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning (NeurIPS 2019)☆24Updated 3 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆89Updated 6 years ago
- Generalizing to unseen domains via distribution matching☆72Updated 5 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆129Updated 5 years ago
- ☆60Updated 3 years ago
- Code for the paper "Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning" (NeurIPS 20)☆73Updated 3 years ago
- Counterfactual Image Generation☆83Updated 5 years ago
- Improving Calibration for Long-Tailed Recognition (CVPR2021)☆149Updated 3 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆38Updated 4 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆289Updated 3 years ago
- Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"☆80Updated 5 years ago
- SKD : Self-supervised Knowledge Distillation for Few-shot Learning☆100Updated 2 years ago
- ☆44Updated 4 years ago
- [ICML 2020] Continuously Indexed Domain Adaptation☆119Updated 3 years ago
- Code implementing the experiments described in the paper "On The Power of Curriculum Learning in Training Deep Networks" by Hacohen & Wei…☆114Updated 5 years ago
- Reproducing experimental results of OOD-by-MCD [Yu and Aizawa et al. ICCV 2019]☆30Updated 5 years ago
- Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in fu…☆53Updated 4 years ago