bhanML / label-noise-papers
An update-to-date list for papers related with label-noise representation learning is here.
☆88Updated 3 years ago
Alternatives and similar repositories for label-noise-papers:
Users that are interested in label-noise-papers are comparing it to the libraries listed below
- Code for the paper "Progressive Identification of True Labels for Partial-Label Learning".☆45Updated 4 years ago
- PyTorch implementation of PiCO https://arxiv.org/abs/2201.08984☆211Updated 11 months ago
- This repository is used to record current noisy label paper in mainstream ML and CV conference and journal.☆35Updated 3 years ago
- AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise☆35Updated 3 years ago
- Meta Label Correction for Noisy Label Learning☆83Updated 2 years ago
- PyTorch implementation of the paper: Long-tail Learning via Logit Adjustment☆101Updated 3 years ago
- Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.☆19Updated 4 years ago
- ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels☆75Updated 3 years ago
- A new code framework that uses pytorch to implement meta-learning, and takes Meta-Weight-Net as an example.☆56Updated 3 years ago
- CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels☆90Updated 2 years ago
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆285Updated 3 years ago
- ICLR 2021, "Learning with feature-dependent label noise: a progressive approach"☆43Updated 2 years ago
- [AAAI 2021] Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning☆137Updated 4 years ago
- Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization☆127Updated 3 years ago
- ☆23Updated 2 years ago
- [AAAI 21] Utilizing meta-learning to correct the noisy labels.☆13Updated 3 years ago
- PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018☆125Updated 5 years ago
- [NeurIPS 2017] [ICML 2019] Code for complementary-label learning☆45Updated 11 months ago
- CVPR'20: Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization☆126Updated last year
- This repository is the official Pytorch implementation of Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Re…☆149Updated 2 years ago
- ☆52Updated 9 months ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆36Updated 3 years ago
- ICML'19 How does Disagreement Help Generalization against Label Corruption?☆83Updated 5 years ago
- Code for CVPR2020 ‘Training Noise Robust Deep Neural Networks via Meta-Learning’☆20Updated 4 years ago
- Source code for NeurIPS 2022 paper SoLar☆27Updated last year
- [ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond☆129Updated last year
- Code release of paper Debiased Self-Training for Semi-Supervised Learning (NeurIPS 2022 Oral)☆51Updated 2 years ago
- "In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Riz…☆232Updated last year
- [ECCV 2022] A generalized long-tailed challenge that incorporates both the conventional class-wise imbalance and the overlooked attribute…☆121Updated 5 months ago