The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
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Updated
Jun 7, 2024 - Python
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
A curated list of resources for Learning with Noisy Labels
NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).
A curated (most recent) list of resources for Learning with Noisy Labels
Curated list of open source tooling for data-centric AI on unstructured data.
Code for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels"
Official Implementation of Early-Learning Regularization Prevents Memorization of Noisy Labels
[ICML2020] Normalized Loss Functions for Deep Learning with Noisy Labels
ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Noise-Tolerant Paradigm for Training Face Recognition CNNs [Official, CVPR 2019]
The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.
NLNL: Negative Learning for Noisy Labels
[ICLR2021] Official Pytorch implementation of "When Optimizing f-Divergence is Robust with Label noise"
Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction (ICANN 2021)
Official Implementation of Unweighted Data Subsampling via Influence Function - AAAI 2020
[NeurIPS 2020] Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
Official data release to reproduce Confident Learning paper results
Official Implementation of the CVPR 2022 paper "UNICON: Combating Label Noise Through Uniform Selection and Contrastive Learning"
PyTorch implementation of "Contrast to Divide: self-supervised pre-training for learning with noisy labels"
PyTorch implementation for Partially View-aligned Representation Learning with Noise-robust Contrastive Loss (CVPR 2021)
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