CBAFed
Pseudo labeling
A PyTorch implementation of a method for improving semi-supervised learning in federated settings by adapting pseudo labels to balance classes.
7 stars
1 watching
0 forks
Language: Python
last commit: about 1 year ago Related projects:
Repository | Description | Stars |
---|---|---|
moucheng2017/med-noisy-labels | Provides PyTorch implementation of a method to address noisy labels in medical image segmentation. | 71 |
mmorafah/pacfl | Implementation of federated learning algorithms for distributed machine learning on private client data | 37 |
diaoenmao/semifl-semi-supervised-federated-learning-for-unlabeled-clients-with-alternate-training | An implementation of semi-supervised federated learning for improving the performance of a server using distributed clients with unlabeled data | 34 |
luuuyi/cbam.pytorch | PyTorch implementation of the CBAM module for refining feature maps in deep networks | 1,337 |
lijunnan1992/dividemix | A PyTorch implementation of a semi-supervised learning framework for training deep neural networks with noisy labels by dynamically dividing the data into clean and noisy sets. | 543 |
kthyeon/fine_official | An implementation of a method for training machine learning models using noisy labels | 38 |
tristandeleu/pytorch-maml-rl | Replication of Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks in PyTorch for reinforcement learning tasks | 827 |
charliedinh/pfedme | An implementation of Personalized Federated Learning with Moreau Envelopes and related algorithms using PyTorch for research and experimentation. | 289 |
zhanghang1989/pytorch-encoding | A Python framework for building deep learning models with optimized encoding layers and batch normalization. | 2,041 |
harliwu/fedamd | This project presents an approach to federated learning with partial client participation by optimizing anchor selection for improving model accuracy and convergence. | 2 |
onlytailei/carla_cil_pytorch | Implementation of a conditional imitation learning policy in PyTorch for autonomous driving using the Carla dataset. | 66 |
pengyang7881187/fedrl | Enabling multiple agents to learn from heterogeneous environments without sharing their knowledge or data | 54 |
chingyaoc/pytorch-reinforce | A PyTorch implementation of the REINFORCE algorithm for reinforcement learning in continuous and discrete environments. | 264 |
xiaoboxia/t-revision | A PyTorch implementation of a method for learning with noisy labels in deep neural networks | 98 |
conditionwang/fcil | Implementation of Federated Class-Incremental Learning for Continual Learning in Computer Vision | 101 |