guixianjin / noisy_label_papers
This repository is used to record current noisy label paper in mainstream ML and CV conference and journal.
☆35Updated 3 years ago
Alternatives and similar repositories for noisy_label_papers:
Users that are interested in noisy_label_papers are comparing it to the libraries listed below
- CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels☆91Updated 2 years ago
- An update-to-date list for papers related with label-noise representation learning is here.☆89Updated 3 years ago
- [AAAI 21] Utilizing meta-learning to correct the noisy labels.☆14Updated 3 years ago
- AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise☆36Updated 3 years ago
- [AAAI 2021] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning☆138Updated 4 years ago
- Exploiting Class Activation Value for Partial-Label Learning, ICLR 2022 (poster)☆14Updated 2 years ago
- Source code for NeurIPS 2022 paper SoLar☆27Updated last year
- Instance-Dependent Partial Label Learning（NIPS'21）；Variational Label Enhancement for Instance-Dependent Partial Label Learning（TPAMI）☆24Updated 6 months ago
- ☆17Updated 3 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆43Updated 2 years ago
- ☆17Updated 2 years ago
- Code for "Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning"☆23Updated 4 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆36Updated 4 years ago
- A pytorch implementation for "Neighborhood Collective Estimation for Noisy Label Identification and Correction", which is accepted by ECC…☆25Updated last year
- Repo for CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning☆99Updated 7 months ago
- PyTorch implementation of the paper "SuperLoss: A Generic Loss for Robust Curriculum Learning" in NIPS 2020.☆30Updated 4 years ago
- Meta Label Correction for Noisy Label Learning☆85Updated 2 years ago
- ☆23Updated 2 years ago
- Code for "Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification", ECCV 2020 Spotlight☆38Updated 4 years ago
- ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels☆76Updated 3 years ago
- NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"☆39Updated 3 years ago
- PyTorch implementation of the paper "Trustworthy Long-Tailed Classification" (CVPR 2022)☆61Updated last year
- ☆16Updated last year
- PyTorch implementation of the paper: Long-tail Learning via Logit Adjustment☆104Updated 3 years ago
- Code for the paper "Progressive Identification of True Labels for Partial-Label Learning".☆47Updated 4 years ago
- Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML 2020☆22Updated 4 years ago
- Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization☆127Updated 3 years ago
- official source code for the Paper: **Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment** based on Pytorch.☆39Updated this week
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆84Updated 5 years ago
- [NeurIPS'20] Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization☆20Updated 2 years ago