The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
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Updated
Jul 2, 2024 - Python
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).
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
The toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling.
ICML 2019: Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Noise-Tolerant Paradigm for Training Face Recognition CNNs [Official, CVPR 2019]
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)
[NeurIPS 2020] Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
Official Implementation of Unweighted Data Subsampling via Influence Function - AAAI 2020
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)
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations (CVPR 2022 Oral)
[ICML2022 Long Talk] Official Pytorch implementation of "To Smooth or Not? When Label Smoothing Meets Noisy Labels"
The official code for the paper "Delving Deep into Label Smoothing", IEEE TIP 2021
The official implementation of the ACM MM'21 paper Co-learning: Learning from noisy labels with self-supervision.
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