Meta Label Correction for Noisy Label Learning
☆86Sep 28, 2022Updated 3 years ago
Alternatives and similar repositories for MLC
Users that are interested in MLC are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- [AAAI 21] Utilizing meta-learning to correct the noisy labels.☆15Apr 26, 2021Updated 5 years ago
- Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.☆21Oct 12, 2020Updated 5 years ago
- A dataset for realistic evaluation of noisy label methods☆15Dec 3, 2023Updated 2 years ago
- pytorch☆10Apr 13, 2022Updated 4 years ago
- AAAI 2021: Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise☆35Jun 9, 2021Updated 5 years ago
- Wordpress hosting with auto-scaling - Free Trial Offer • AdFully Managed hosting for WordPress and WooCommerce businesses that need reliable, auto-scalable performance. Cloudways SafeUpdates now available.
- NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).☆296Dec 14, 2021Updated 4 years ago
- ☆24Oct 14, 2022Updated 3 years ago
- Official implementation of "Open-set Label Noise Can Improve Robustness Against Inherent Label Noise" (NeurIPS 2021)☆20Jul 5, 2022Updated 3 years ago
- A new code framework that uses pytorch to implement meta-learning, and takes Meta-Weight-Net as an example.☆63Aug 28, 2021Updated 4 years ago
- NeurIPS 2022: Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning☆18Mar 3, 2023Updated 3 years ago
- Q. Yao, H. Yang, B. Han, G. Niu, J. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. ICML 2020☆23Aug 23, 2020Updated 5 years ago
- This repo consists of collection of papers and repos on the topic of deep learning by noisy labels / label noise.☆236Sep 20, 2021Updated 4 years ago
- Is BERT Robust to Label Noise? A Study on Learning with Noisy Labels in Text Classification☆10May 31, 2022Updated 4 years ago
- Code for paper: DivideMix: Learning with Noisy Labels as Semi-supervised Learning☆577Sep 14, 2020Updated 5 years ago
- AI Agents on DigitalOcean Gradient AI Platform • AdBuild production-ready AI agents using customizable tools or access multiple LLMs through a single endpoint. Create custom knowledge bases or connect external data.
- ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels☆78Jun 15, 2021Updated 4 years ago
- Code for AAAI 2021 long paper Learning from Crowds by Modeling Common Confusions.☆11Feb 6, 2021Updated 5 years ago
- CVPR 2022: Selective-Supervised Contrastive Learning with Noisy Labels☆95Mar 28, 2022Updated 4 years ago
- PyTorch implementation of the paper "SuperLoss: A Generic Loss for Robust Curriculum Learning" in NIPS 2020.☆29Jan 26, 2021Updated 5 years ago
- NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels☆520Aug 19, 2021Updated 4 years ago
- Pytorch implementation for Deep Self-Learning From Noisy Labels☆33Apr 7, 2020Updated 6 years ago
- ☆17Nov 27, 2023Updated 2 years ago
- PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"☆70Mar 30, 2021Updated 5 years ago
- Code for CVPR2020 ‘Training Noise Robust Deep Neural Networks via Meta-Learning’☆20Oct 6, 2020Updated 5 years ago
- Managed Kubernetes at scale on DigitalOcean • AdDigitalOcean Kubernetes includes the control plane, bandwidth allowance, container registry, automatic updates, and more for free.
- An implementation of the paper "Learning to Reweight Examples for Robust Deep Learning" from ICML 2018 with PyTorch and Higher.☆28Oct 11, 2022Updated 3 years ago
- [ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels☆141Jul 5, 2024Updated last year
- Keras implementation of Training Deep Neural Networks on Noisy Labels with Bootstrapping, Reed et al. 2015☆22Jan 28, 2021Updated 5 years ago
- ☆32Apr 22, 2021Updated 5 years ago
- SMiLER - Samsung MultiLingual Entity and Relation Extraction dataset☆18Feb 11, 2021Updated 5 years ago
- The official code for the NeurIPS 2021 paper Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels (https://arxiv.org…☆25Dec 29, 2021Updated 4 years ago
- The code for lifelong few-shot language learning☆55Feb 17, 2022Updated 4 years ago
- The source code for 'Noisy-Labeled NER with Confidence Estimation' accepted by NAACL 2021☆36May 8, 2021Updated 5 years ago
- Code for CoMatch: Semi-supervised Learning with Contrastive Graph Regularization☆133May 1, 2025Updated last year
- Managed Database hosting by DigitalOcean • AdPostgreSQL, MySQL, MongoDB, Kafka, Valkey, and OpenSearch available. Automatically scale up storage and focus on building your apps.
- [ACL 2023] The code for our ACL'23 paper Cold-Start Data Selection for Few-shot Language Model Fine-tuning: A Prompt-Based Uncertainty Pr…☆24Jun 1, 2024Updated 2 years ago
- ☆38Jul 13, 2020Updated 5 years ago
- Code for EMNLP2019 paper : "Benchmarking zero-shot text classification: datasets, evaluation and entailment approach"☆18Jul 9, 2022Updated 3 years ago
- A Survey☆572Feb 13, 2023Updated 3 years ago
- Knowledge Infused Decoding☆70Dec 31, 2023Updated 2 years ago
- ICLR 2021: Noise against noise: stochastic label noise helps combat inherent label noise☆15May 1, 2021Updated 5 years ago
- ☆28Jan 6, 2020Updated 6 years ago