CDR
Label noise mitigation method
An implementation of a PyTorch-based deep learning method to improve robustness against noisy labels in image classification tasks
ICLR‘2021: Robust Early-learning: Hindering the Memorization of Noisy Labels
75 stars
4 watching
10 forks
Language: Python
last commit: over 3 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| A PyTorch implementation of a method for learning with noisy labels in deep neural networks | 97 |
| Develops and evaluates machine learning algorithms to mitigate the effects of noisy labels in supervised learning. | 30 |
| An implementation of a method to improve classification accuracy on noisy labels by reweighting their importance | 39 |
| An implementation of a method to improve model robustness against inherent label noise in machine learning models | 19 |
| A method to train deep learning classifiers on noisy labels using a small set of trusted data | 86 |
| An implementation of gradient clipping as a method to mitigate the effects of noisy labels in machine learning models | 14 |
| An implementation of a method to learn with instance-dependent label noise in deep learning models using PyTorch | 47 |
| A repository providing code and scripts for training image classification models on noisy labeled data | 116 |
| An implementation of training deep neural networks on noisy labels with bootstrapping using Keras | 22 |
| Implementation of a method to improve machine learning models trained with noisy labels by selecting and collaborating with high-quality samples | 39 |
| An investigation into deep learning models trained with noisy labels and methods to improve their accuracy. | 90 |
| This project explores how to adapt neural networks to noisy labels by introducing a mechanism that can learn to correct the errors. | 118 |
| Provides tools and data for studying instance-dependent label noise in deep neural networks, with a focus on combating noisy labels | 35 |
| Develops a robust learning framework to handle noisy labels in multimodal data and improve cross-modal retrieval. | 13 |
| An implementation of an unsupervised label noise modeling and loss correction approach for deep learning. | 221 |