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:
- Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.☆19Updated 4 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆85Updated 5 years ago
- AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise☆35Updated 4 years ago
- CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels☆93Updated 3 years ago
- [AAAI 21] Utilizing meta-learning to correct the noisy labels.☆15Updated 4 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆37Updated 4 years ago
- NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"☆39Updated 3 years ago
- ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels☆76Updated 4 years ago
- An update-to-date list for papers related with label-noise representation learning is here.☆90Updated 3 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆34Updated 4 years ago
- A new code framework that uses pytorch to implement meta-learning, and takes Meta-Weight-Net as an example.☆60Updated 3 years ago
- Learning from Failure: Training Debiased Classifier from Biased Classifier (NeurIPS 2020)☆91Updated 4 years ago
- ☆29Updated 2 years ago
- MoPro: Webly Supervised Learning☆87Updated last month
- Instance-Dependent Partial Label Learning(NIPS'21);Variational Label Enhancement for Instance-Dependent Partial Label Learning(TPAMI)☆25Updated 3 weeks 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
- Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization☆127Updated last month
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆43Updated 2 years ago
- ☆16Updated last year
- Official Implementation of Robust Training under Label Noise by Over-parameterization☆64Updated 2 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- [ICML 2021] “ Self-Damaging Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Bobak Mortazavi, Zhangyang Wang☆63Updated 3 years ago
- ☆35Updated 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
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆137Updated 11 months ago
- Code for the paper Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift☆37Updated 4 years ago
- [NeurIPS 2017] [ICML 2019] Code for complementary-label learning☆48Updated last year
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆289Updated 3 years ago
- Code for the paper "Progressive Identification of True Labels for Partial-Label Learning".☆51Updated 4 years ago
- This repository is used to record current noisy label paper in mainstream ML and CV conference and journal.☆35Updated 3 years ago