VL-VFE

This repository contains the codes for the variable-length variational feature encoding (VL-VFE) method proposed in the paper "Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Method", which is accepted to IEEE Journal on Selected Areas in Communications.

Dependencies

Packages

Pytorch 1.8.1
Torchvision 0.9.1

Datasets

MNIST
CIFAR-10

How to run

Train the VL-VFE method on the MNIST dataset

python VL_VFE_MNIST.py --intermediate_dim 64 --beta 6e-3 --threshold 1e-2

Train the VL-VFE method on the CIFAR dataset

python VL_VFE_CIFAR.py --intermediate_dim 64 --beta 9e-3 --threshold 1e-2

The parameter intermediate_dim denotes the (maximum) dimension of the encoded feature vector. The weighting factor beta and the pruning threshold threshold control the tradeoff between the accuracy and the number of activated dimensions.

Inference

After training the neural network, we can test the performance under different channel conditions --channel_noise, which represents the standard deviation in the Gaussian distribution. The relationship between the --channel_noise and the peak signal-to-noise ratio (PSNR) is summarized as follows:

channel_noise	0.3162	0.2371	0.1778	0.1334	0.1000	0.0750	0.0562
PSNR	10 dB	12.5 dB	15 dB	17.5 dB	20 dB	22.5 dB	25 dB

Test the VL-VFE method on the MNIST dataset with PSNR=20 dB

python3 VL_VFE_MNIST.py --test 1 --intermediate_dim 64 --channel_noise 0.1 --threshold 1e-2 --weights ./pretrained/model/location

Test the VL-VFE method on the CIFAR dataset with PSNR=20 dB

python3 VL_VFE_CIFAR.py --test 1 --intermediate_dim 64 --channel_noise 0.1 --threshold 1e-2 --weights ./pretrained/model/location

Several pretrained models and results are shown in Examples.

Citation

@article{shao2021learning,  
  author={Shao, Jiawei and Mao, Yuyi and Zhang, Jun},  
  journal={IEEE Journal on Selected Areas in Communications},  
  title={Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach},   
  year={2022},  
  volume={40},  
  number={1},  
  pages={197-211},  
  doi={10.1109/JSAC.2021.3126087}}

Others

• The variational feature encoding (VFE) proposed in this paper can be achieved by replacing the function self.gamma_mu = gamma_function() with a vector self.mu = nn.Parameter(torch.ones(args.intermediate_dim)) and fixing the channel noise level in the training process.

• Known problem: The loss may become NaN when training the network on the CIFAR dataset.

• (2023-Jan-19) I have corrected some typos and updated the paper on arxiv.