lnl_sr
Label regularization
An implementation of a regularization technique to improve the accuracy of deep learning models trained with noisy labels.
Learning with Noisy Labels via Sparse Regularization, ICCV2021
46 stars
2 watching
4 forks
Language: Python
last commit: over 2 years ago iccv2021pytorch
Related projects:
Repository | Description | Stars |
---|---|---|
paulalbert31/labelnoisecorrection | An implementation of an unsupervised label noise modeling and loss correction approach for deep learning. | 220 |
ucsc-real/cores | An implementation of a method to learn from noisy labels in machine learning models with instance-dependent noise | 36 |
xlearning-scu/2021-cvpr-mrl | Develops a robust learning framework to handle noisy labels in multimodal data and improve cross-modal retrieval. | 13 |
xiaoboxia/t-revision | A PyTorch implementation of a method for learning with noisy labels in deep neural networks | 98 |
dr-darryl-wright/noisy-labels-with-bootstrapping | An implementation of training deep neural networks on noisy labels with bootstrapping using Keras | 22 |
chenpf1025/idn | Provides tools and data for studying instance-dependent label noise in deep neural networks, with a focus on combating noisy labels | 35 |
pingqingsheng/lrt | An algorithm designed to robustly correct noisy labels in training data by iteratively refining the network's confidence and updating the loss function. | 21 |
ijindal/noisy_dropout_regularization | This project explores training deep neural networks using noisy labels with dropout regularization to improve robustness. | 11 |
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 |
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 |
mmazeika/glc | A method to train deep learning classifiers on noisy labels using a small set of trusted data | 86 |
chenpf1025/noisy_label_understanding_utilizing | An investigation into deep learning models trained with noisy labels and methods to improve their accuracy. | 90 |
hongxin001/odnl | An implementation of a method to improve model robustness against inherent label noise in machine learning models | 19 |
moucheng2017/med-noisy-labels | Provides PyTorch implementation of a method to address noisy labels in medical image segmentation. | 71 |