The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
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Updated
May 23, 2024 - Python
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Client interface for all things Cleanlab Studio
Cifar with Noisy from Human or Synthesis
A curated list of resources for Learning with Noisy Labels
The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.
Code for "From Instance to Metric Calibration: A Unified Framework for Open-World Few-Shot Learning" in TPAMI 2023.
This is a summary of research on noisy correspondence. There may be omissions. If anything is missing please get in touch with us. Our emails: linyijie.gm@gmail.com yangmouxing@gmail.com qinyang.gm@gmail.com
A curated (most recent) list of resources for Learning with Noisy Labels
SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise (BMVC2022)
A Label Studio plugin with InstanceGM for improving data labels for machine learning with machine learning
Code associated to the article "Multi-annotator Deep Learning: A Probabilistic Framework for Classification"
Code associated to the article "Who knows best? Intelligent Crowdworker Selection via Deep Learning"
A curated list of papers that study learning with noisy labels.
Exploring Parity Challenges in Reinforcement Learning through Curriculum Learning with Noisy Labels
Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning"
Curated list of open source tooling for data-centric AI on unstructured data.
Code for the KDD-2023 paper: Neural-Hidden-CRF: A Robust Weakly-Supervised Sequence Labeler
Official PyTorch implementation of the paper "Robust Training for Speaker Verification against Noisy Labels" in INTERSPEECH 2023.
[ECCV 2022] Robust Object Detection With Inaccurate Bounding Boxes
A tool for automatically labelling discharge summaries into disease categories.
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