CAL
Label noise trainer
An implementation of a method to learn with instance-dependent label noise in deep learning models using PyTorch
A Second-Order Approach to Learning with Instance-Dependent Label Noise (CVPR'21 oral)
47 stars
3 watching
9 forks
Language: Python
last commit: almost 4 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
| ucsc-real/cores | An implementation of a method to learn from noisy labels in machine learning models with instance-dependent noise | 36 |
 nust-machine-intelligence-laboratory/jo-src | An implementation of a contrastive learning approach to address noisy labels in machine learning models | 5 |
 paulalbert31/labelnoisecorrection | An implementation of an unsupervised label noise modeling and loss correction approach for deep learning. | 221 |
 xlearning-scu/2021-cvpr-mrl | Develops a robust learning framework to handle noisy labels in multimodal data and improve cross-modal retrieval. | 13 |
 chenpf1025/noisy_label_understanding_utilizing | An investigation into deep learning models trained with noisy labels and methods to improve their accuracy. | 90 |
 xiaoboxia/cdr | An implementation of a PyTorch-based deep learning method to improve robustness against noisy labels in image classification tasks | 75 |
| chenpf1025/idn | Provides tools and data for studying instance-dependent label noise in deep neural networks, with a focus on combating noisy labels | 35 |
 hongxin001/odnl | An implementation of a method to improve model robustness against inherent label noise in machine learning models | 19 |
 cysu/noisy_label | A repository providing code and scripts for training image classification models on noisy labeled data | 116 |
 pxiangwu/topofilter | Develops and evaluates machine learning algorithms to mitigate the effects of noisy labels in supervised learning. | 30 |
 uds-lsv/multi-tasking_learning_with_unreliable_labels | An open source software project that extends an existing algorithm to handle noisy labels in machine learning for low-resource data generation. | 8 |
| xiaoboxia/t-revision | A PyTorch implementation of a method for learning with noisy labels in deep neural networks | 97 |
 moucheng2017/med-noisy-labels | Provides PyTorch implementation of a method to address noisy labels in medical image segmentation. | 71 |
 udibr/noisy_labels | This project explores how to adapt neural networks to noisy labels by introducing a mechanism that can learn to correct the errors. | 118 |
 hitcszx/lnl_sr | An implementation of a regularization technique to improve the accuracy of deep learning models trained with noisy labels. | 46 |