ShikunLi / Sel-CLView external linksLinks
CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels
☆95Mar 28, 2022Updated 3 years ago
Alternatives and similar repositories for Sel-CL
Users that are interested in Sel-CL are comparing it to the libraries listed below
Sorting:
- ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels☆78Jun 15, 2021Updated 4 years ago
- Official implementation for: "Multi-Objective Interpolation Training for Robustness to Label Noise"☆41May 13, 2022Updated 3 years ago
- [CVPR'22] Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learnin…☆62Oct 10, 2024Updated last year
- The official implementation of CVPR2023 paper "DISC: Learning from Noisy Labels via Dynamic Instance-Specific Selection and Correction"☆55Jul 19, 2023Updated 2 years ago
- NeurIPS 2022: Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning☆18Mar 3, 2023Updated 2 years ago
- pytorch☆10Apr 13, 2022Updated 3 years ago
- PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"☆71Mar 30, 2021Updated 4 years ago
- NeurIPS 2021, "Fine Samples for Learning with Noisy Labels"☆41Nov 29, 2021Updated 4 years ago
- A PyTorch-based library for On Learning Contrastive Representations for Learning With Noisy Labels (CVPR'22)☆44Sep 8, 2022Updated 3 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆46Oct 29, 2022Updated 3 years ago
- Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels☆300May 22, 2023Updated 2 years ago
- ICLR 2021: Noise against noise: stochastic label noise helps combat inherent label noise☆15May 1, 2021Updated 4 years ago
- [NeurIPS 2022] "Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks"☆13Nov 11, 2022Updated 3 years ago
- ☆25May 31, 2022Updated 3 years ago
- ☆17Nov 27, 2023Updated 2 years ago
- PyTorch implementation of PiCO https://arxiv.org/abs/2201.08984☆225Feb 3, 2024Updated 2 years ago
- TPAMI: Classification with noisy labels by importance reweighting.☆40Oct 4, 2019Updated 6 years ago
- A curated (most recent) list of resources for Learning with Noisy Labels☆717Oct 18, 2024Updated last year
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆141Jul 5, 2024Updated last year
- self ensemble label correction☆17Jul 29, 2022Updated 3 years ago
- [AAAI 21] Utilizing meta-learning to correct the noisy labels.☆15Apr 26, 2021Updated 4 years ago
- ☆20May 1, 2025Updated 9 months ago
- NeurIPS'2020: Part-dependent Label Noise: Towards Instance-dependent Label Noise☆61Dec 16, 2020Updated 5 years ago
- Source code for NeurIPS 2022 paper SoLar☆30Dec 20, 2023Updated 2 years ago
- noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.☆59Aug 7, 2022Updated 3 years ago
- ☆34Sep 15, 2021Updated 4 years ago
- [NeurIPS 2017] [ICML 2019] Code for complementary-label learning☆50Aug 17, 2025Updated 5 months ago
- Code for the paper "Progressive Identification of True Labels for Partial-Label Learning".☆52Aug 21, 2020Updated 5 years ago
- [ICLR 2023] PyTorch implementation for "Long-Tailed Partial Label Learning via Dynamic Rebalancing"☆58Apr 21, 2023Updated 2 years ago
- Twin Contrastive Learning with Noisy Labels (CVPR 2023)