pingqingsheng / LRTLinks
☆24Updated 2 years ago
Alternatives and similar repositories for LRT
Users that are interested in LRT are comparing it to the libraries listed below
Sorting:
- [ICLR 2021] Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization☆41Updated 4 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆43Updated 2 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆37Updated 4 years ago
- PyTorch implementation of the paper "SuperLoss: A Generic Loss for Robust Curriculum Learning" in NIPS 2020.☆29Updated 4 years ago
- Code release for Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning (NeurIPS 2019)☆24Updated 3 years ago
- PyTorch implementation of the paper "Trustworthy Long-Tailed Classification" (CVPR 2022)☆61Updated last year
- Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.☆19Updated 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
- This is the official repo for our CVPR22 paper: Scalable Penalized Regression for Noise Detection in Learning With Noisy Labels.☆18Updated last year
- [NeurIPS 2021] “Improving Contrastive Learning on Imbalanced Data via Open-World Sampling”, Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangya…☆28Updated 3 years ago
- NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"☆39Updated 3 years ago
- Paper and Code for "Curriculum Learning by Optimizing Learning Dynamics" (AISTATS 2021)☆19Updated 4 years ago
- Learning with Noisy Labels, Label Noise, ICML 2021☆45Updated 2 years ago
- NeurIPS 2022: Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning☆17Updated 2 years ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- ☆28Updated 3 years ago
- ☆32Updated 4 years ago
- Code for "Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification", ECCV 2020 Spotlight☆38Updated 4 years ago
- [ICASSP 2020] Code release of paper 'Heterogeneous Domain Generalization via Domain Mixup'☆26Updated 4 years ago
- Meta Label Correction for Noisy Label Learning☆85Updated 2 years ago
- Code for "Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning"☆24Updated 4 years ago
- Official Implementation of Robust Training under Label Noise by Over-parameterization☆64Updated 2 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
- 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
- Weakly Supervised Contrastive Learning☆41Updated 3 years ago
- ICML'20: SIGUA: Forgetting May Make Learning with Noisy Labels More Robust☆15Updated 4 years ago
- Code of our method MbLS (Margin-based Label Smoothing) for network calibration. To Appear at CVPR 2022. Paper : https://arxiv.org/abs/211…☆50Updated 2 years ago
- Learning with Noisy Labels via Sparse Regularization, ICCV2021☆46Updated 3 years ago
- Code implementation for paper "On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals".☆16Updated 3 years ago