noisy_labels
Label correction mechanism
This project explores how to adapt neural networks to noisy labels by introducing a mechanism that can learn to correct the errors.
TRAINING DEEP NEURAL-NETWORKS USING A NOISE ADAPTATION LAYER
118 stars
6 watching
38 forks
Language: Jupyter Notebook
last commit: over 7 years ago
Linked from 2 awesome lists
subeeshvasu/awesome-learning-with-label-noise
songhwanjun/awesome-noisy-labelsRelated projects:
| Repository | Description | Stars |
|---|---|---|
 dr-darryl-wright/noisy-labels-with-bootstrapping | An implementation of training deep neural networks on noisy labels with bootstrapping using Keras | 22 |
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 paulalbert31/labelnoisecorrection | An implementation of an unsupervised label noise modeling and loss correction approach for deep learning. | 220 |
 vdenberg/noisy-label-neural-network | An implementation of a neural network algorithm designed to improve performance on noisy labeled data | 3 |
 moucheng2017/med-noisy-labels | Provides PyTorch implementation of a method to address noisy labels in medical image segmentation. | 71 |
 cysu/noisy_label | A repository providing code and scripts for training image classification models on noisy labeled data | 115 |
 weijiaheng/advances-in-label-noise-learning | A curated collection of papers and resources on learning with noisy labels in machine learning | 687 |
 ryo-ito/noisy-labels-neural-network | An implementation of a neural network training method using noisy labels | 5 |
 delchiaro/training-cnn-noisy-labels-keras | An implementation of a deep learning training method for handling noisy labels in convolutional neural networks using the VGG-16 network architecture. | 6 |
 nust-machine-intelligence-laboratory/jo-src | An implementation of a contrastive learning approach to address noisy labels in machine learning models | 5 |
| xiaoboxia/cdr | An implementation of a PyTorch-based deep learning method to improve robustness against noisy labels in image classification tasks | 75 |
 hongxin001/odnl | An implementation of a method to improve model robustness against inherent label noise in machine learning models | 19 |