Truncated-Loss

Noisy label loss function

An implementation of a loss function designed to improve the training of deep neural networks with noisy labels

PyTorch implementation of the paper "Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels" in NIPS 2018

GitHub

126 stars
3 watching
9 forks
Language: Python
last commit: almost 7 years ago

Related projects:

RepositoryDescriptionStars
coincheung/pytorch-lossProvides a comprehensive set of implementation of various loss functions and operators for deep learning models2,196
hanxunh/active-passive-lossesA PyTorch-based framework for implementing normalized loss functions to improve deep learning model robustness against noisy labels.134
mblondel/fenchel-young-lossesProvides Fenchel-Young losses for probabilistic classification in PyTorch/TensorFlow/scikit-learn.183
dr-darryl-wright/noisy-labels-with-bootstrappingAn implementation of training deep neural networks on noisy labels with bootstrapping using Keras22
xiaoboxia/t-revisionA PyTorch implementation of a method for learning with noisy labels in deep neural networks97
ijindal/noisy_dropout_regularizationThis project explores training deep neural networks using noisy labels with dropout regularization to improve robustness.11
chenpf1025/idnProvides tools and data for studying instance-dependent label noise in deep neural networks, with a focus on combating noisy labels35
uds-lsv/multi-tasking_learning_with_unreliable_labelsAn open source software project that extends an existing algorithm to handle noisy labels in machine learning for low-resource data generation.8
hitcszx/lnl_srAn implementation of a regularization technique to improve the accuracy of deep learning models trained with noisy labels.46
bes-dev/mpl.pytorchA PyTorch implementation of a loss function used in semantic image segmentation175
cysu/noisy_labelA repository providing code and scripts for training image classification models on noisy labeled data116
pxiangwu/topofilterDevelops and evaluates machine learning algorithms to mitigate the effects of noisy labels in supervised learning.30
delchiaro/training-cnn-noisy-labels-kerasAn implementation of a deep learning training method for handling noisy labels in convolutional neural networks using the VGG-16 network architecture.6
paulalbert31/labelnoisecorrectionAn implementation of an unsupervised label noise modeling and loss correction approach for deep learning.221
vdenberg/noisy-label-neural-networkAn implementation of a neural network algorithm designed to improve performance on noisy labeled data3