self-adaptive-training

Generalization booster

Improves deep network generalization under noise and enhances self-supervised representation learning

[TPAMI2022 & NeurIPS2020] Official implementation of Self-Adaptive Training

GitHub

127 stars
4 watching
23 forks
Language: Python
last commit: about 3 years ago
adversarial-robustnesscomputer-visiongeneralizationlabel-noisemachine-learningoverfitting

Related projects:

Repository Description Stars
google-research/noisystudent A semi-supervised learning method to improve the accuracy of machine learning models by using noisy teacher models and student models. 753
eth-sri/diffai Trains neural networks to be provably robust against adversarial examples using abstract interpretation techniques. 218
mit-han-lab/data-efficient-gans Improves GAN training efficiency by incorporating data augmentation 1,283
yerevann/warp An approach to transfer learning for NLP tasks using adversarial reprogramming and word-level task-specific embeddings. 83
ahmedfgad/neuralgenetic Tools and techniques for training neural networks using genetic algorithms 240
ahmedfgad/cnngenetic Trains convolutional neural networks using the genetic algorithm 22
tmllab/2021_neurips_pes Improves the performance of deep neural networks by selectively stopping training at different stages 29
stormraiser/gan-weight-norm Improves the performance of Generative Adversarial Networks by normalizing weights and batch data 181
kentonishi/augmentation-for-lnl Provides a framework for learning with noisy labels using data augmentation strategies. 113
loudinthecloud/dpwa A distributed learning framework that enables peer-to-peer parameter averaging and asynchronous training of deep neural networks 53
pistony/residualattentionnetwork A Gluon implementation of Residual Attention Network for image classification tasks 107
bupt-ai-cz/meta-selflearning Develops a method to improve performance of computer vision tasks by adapting models to new domains and data sources through meta-learning and self-learning techniques. 199
akanimax/pro_gan_pytorch Implementation of a deep learning model for generating high-quality images with improved stability and variation. 536
gbdt-pl/gbdt-pl An implementation of a gradient boosting algorithm with piece-wise linear regression trees for efficient machine learning model training 149
warrengreen/srcnn Software for enhancing satellite images through deep learning techniques 76