WuYichen-97 / Learning-to-Purify-Noisy-Labels-via-Meta-Soft-Label-CorrectorLinks
[AAAI 21] Utilizing meta-learning to correct the noisy labels.
☆15Updated 4 years ago
Alternatives and similar repositories for Learning-to-Purify-Noisy-Labels-via-Meta-Soft-Label-Corrector
Users that are interested in Learning-to-Purify-Noisy-Labels-via-Meta-Soft-Label-Corrector are comparing it to the libraries listed below
Sorting:
- ☆35Updated 3 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
- CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels☆93Updated 3 years ago
- This is an official PyTorch implementation of the ICML 2023 paper AdaNPC and SIGKDD paper DRM.☆85Updated last year
- Source code for NeurIPS 2022 paper SoLar☆29Updated last year
- ☆58Updated 2 years ago
- ☆29Updated 2 years ago
- ☆61Updated 3 years ago
- [ECCV 2022] Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization, and Beyond☆136Updated 2 years ago
- Meta Label Correction for Noisy Label Learning☆85Updated 2 years ago
- Code release for "Cycle Self-Training for Domain Adaptation" (NeurIPS 2021)☆53Updated 3 years ago
- Learning with Instance-Dependent Label Noise: A Sample Sieve Approach (ICLR2021)☆37Updated 4 years ago
- [ICML'2022] Estimating Instance-dependent Bayes-label Transition Matrix using a Deep Neural Network☆19Updated 2 years ago
- A pytorch implementation for "Neighborhood Collective Estimation for Noisy Label Identification and Correction", which is accepted by ECC…☆25Updated 2 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
- Code for CVPR2020 ‘Training Noise Robust Deep Neural Networks via Meta-Learning’☆20Updated 4 years ago
- Co^2L: Contrastive Continual Learning (ICCV2021)☆96Updated 3 years ago
- Instance-Dependent Partial Label Learning（NIPS'21）；Variational Label Enhancement for Instance-Dependent Partial Label Learning（TPAMI）☆25Updated 3 weeks ago
- Official Implementation of Robust Training under Label Noise by Over-parameterization☆64Updated 2 years ago
- Code release of paper Debiased Self-Training for Semi-Supervised Learning (NeurIPS 2022 Oral)☆53Updated 2 years ago
- PyTorch implementation of our NeurIPS 2021 paper "Class-Incremental Learning via Dual Augmentation"☆37Updated 2 years ago
- Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.☆19Updated 4 years ago
- Exploiting Domain-Specific Features to Enhance Domain Generalization (NeurIPS 2021).☆28Updated 3 years ago
- [NeurIPS 2022] The official code for our NeurIPS 2022 paper "Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnab…☆45Updated 2 years ago
- Code for the paper "Representational Continuity for Unsupervised Continual Learning" (ICLR 22)☆96Updated 2 years ago
- Code for TMLR 2023 paper "OpenCon: Open-world Contrastive Learning"☆37Updated 2 years ago
- This codebase is the official implementation of Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization (NeurIPS2…☆99Updated 3 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