microsoft / MLCLinks
Meta Label Correction for Noisy Label Learning
☆85Updated 2 years ago
Alternatives and similar repositories for MLC
Users that are interested in MLC are comparing it to the libraries listed below
Sorting:
- AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise☆35Updated 4 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆37Updated 4 years ago
- Source code for paper "Contrastive Out-of-Distribution Detection for Pretrained Transformers", EMNLP 2021☆40Updated 3 years ago
- An update-to-date list for papers related with label-noise representation learning is here.☆90Updated 3 years ago
- Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.☆19Updated 4 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆128Updated 5 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆85Updated 6 years ago
- PyTorch implementation of consistency regularization methods for semi-supervised learning☆78Updated 4 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆34Updated 4 years ago
- [AAAI 21] Utilizing meta-learning to correct the noisy labels.☆15Updated 4 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- Instance-Dependent Partial Label Learning（NIPS'21）；Variational Label Enhancement for Instance-Dependent Partial Label Learning（TPAMI）☆25Updated last month
- NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"☆39Updated 3 years ago
- Learning from Failure: Training Debiased Classifier from Biased Classifier (NeurIPS 2020)☆91Updated 4 years ago
- PyTorch implementation of PiCO https://arxiv.org/abs/2201.08984☆218Updated last year
- ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels☆76Updated 4 years ago
- A new code framework that uses pytorch to implement meta-learning, and takes Meta-Weight-Net as an example.☆61Updated 3 years ago
- Code implementing the experiments described in the paper "On The Power of Curriculum Learning in Training Deep Networks" by Hacohen & Wei…☆112Updated 5 years ago
- [NeurIPS 2017] [ICML 2019] Code for complementary-label learning☆48Updated last year
- ICLR 2021, Contrastive Learning with Hard Negative Samples☆270Updated 3 years ago
- ☆108Updated 2 years ago
- [AAAI 2021] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning☆139Updated 4 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆290Updated 3 years ago
- MoPro: Webly Supervised Learning☆87Updated 2 months ago
- CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels☆93Updated 3 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
- Source code for NeurIPS 2022 paper SoLar☆29Updated last year
- This is the code for the paper Bayesian Invariant Risk Minmization of CVPR 2022.☆44Updated 2 years ago
- Code for "Just Train Twice: Improving Group Robustness without Training Group Information"☆72Updated last year
- ☆16Updated last year