Transfer-for-DVS in Pytorch

Here is the PyTorch implementation of our paper.

Paper Title: "An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain"

Authors: Xiang He*, Dongcheng Zhao*, Yang Li*, Guobin Shen, Qingqun Kong, Yi Zeng

Accepted by: The 38th Annual AAAI Conference on Artificial Intelligence (AAAI 2024, Oral Presentation)

Why we need a transfer

• Event-based datasets are usually less annotated, and the small data scale makes SNNs prone to overfitting

• While static images intuitively provide rich spatial information that may benefit event data, exploiting this knowledge remains a difficult problem. This is because that static and event data represent different modalities with domain mismatch.

Method Introduction

•To address this problem, we propose solutions in terms of two aspects: feature distribution and training strategy.

Knowledge Transfer Loss

•Learn spatial domain-invariant features and provides dynamically learnable coefficients

•Reduce domain distribution differences
Sliding Training

•Static image inputs are gradually replaced with event data probabilistically during training process

•Result in a smoother and more stable learning process.

Usage

The well-trained model can be found at here.

If you want to retrain yourself to verify the results in the paper, please refer to the commands in scripts run_aba.sh and run_omni.sh.

As an example, the script for using our method on the N-Caltech101 dataset would look like this:

python main_transfer.py --model Transfer_VGG_SNN --node-type LIFNode --source-dataset CALTECH101 --target-dataset NCALTECH101 --step 10 --batch-size 120 --act-fun QGateGrad --device 4 --seed 42 --num-classes 101 --traindata-ratio 1.0 --smoothing 0.0 --domain-loss --domain-loss-coefficient 0.5 --TET-loss --regularization

For datasets CEP-DVS, The corresponding read file is rgb_hsv.py, Please put it in the tonic environment at a location such as: /home/anaconda3/envs/all_hx/lib/python3.8/site-packages/tonic/datasets/.

Citation

If our paper is useful for your research, please consider citing it:

@inproceedings{he2024efficient,
  title={An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain},
  author={He, Xiang and Zhao, Dongcheng and Li, Yang and Shen, Guobin and Kong, Qingqun and Zeng, Yi},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={1},
  pages={512--520},
  year={2024}
}

Acknowledgements

This code began with Brain-Cog, and the code for the visualization is from https://github.com/tomgoldstein/loss-landscape and https://github.com/jacobgil/pytorch-grad-cam . Thanks for their great work. If you are confused about using it or have other feedback and comments, please feel free to contact us via hexiang2021@ia.ac.cn. Have a good day!

Brain-Cog-Lab / Transfer-for-DVS