semantic_extraction

The semantic extraction part of the proposed method in "Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data".

Notes

The folder is for image classification task with the MNIST and CIFAR10 datasets. The image segmentation task with the PASCAL-VOC dataset is in the sub-folder VOC.

Quick Start

Train the Classifier (Pragmatic Function)

1) For the MNIST dataset

$ python MLP_MNIST_model.py 

2) For the CIFAR10 dataset

$ python googlenet_train.py 

Train the Semantic Extraction Part

1) For the MNIST dataset

$ python MNIST.py --alpha xx --pretrain_epoch xx --random_seed xx

2) For the CIFAR10 dataset

$ python CIFAR.py --alpha xx --pretrain_epoch xx --random_seed xx

Some Results

:

:

:

:

:

:

:

:

:

:

Citation

Please use the following BibTeX citation if you use this repository in your work:

@article{Deep_semantic_comm_2022,
  title={Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Data},
  author={Zhang, Hongwei and Shao, Shuo and Tao, Meixia and Bi, Xiaoyan and Letaief, Khaled B},
  journal={arXiv preprint arXiv:2205.00271},
  year={2022}
}