⭐We have provided PyTorch and CUDA versions of the SCI method's code, which can be found in BRIEF_PyTorch and BRIEF_CUDA, respectively.

Our paper was accepted to AAAI2023. You can also find our full-version paper on arXiv

git clone [email protected]:RichealYoung/ImplicitNeuralCompression.git
cd ImplicitNeuralCompression

conda create -n inc python=3.10
conda activate inc

pip3 install -r requirements.txt

python sci.py -c config/SingleExp/sci.yaml -g 2

All hyper-parameters can be set in the YAML file.

❗Note: The partition methods will be released soon!

We have also provided a useful script 'BatchExp.py' for researchers to perform batch experiments quickly. You just need to configure this group of experiments in the YAML file, pick the GPUs, and start batch experiments with the following command.

python BatchExp.py -c config/BatchExp/sci.yaml -stp sci.py -g 0,1,2,3

These experiments will automatically wait or execute depending on GPU utilization..

@inproceedings{yang2023sci,
  title={Sci: A spectrum concentrated implicit neural compression for biomedical data},
  author={Yang, Runzhao and Xiao, Tingxiong and Cheng, Yuxiao and Cao, Qianni and Qu, Jinyuan and Suo, Jinli and Dai, Qionghai},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={4},
  pages={4774--4782},
  year={2023}
}

If you need any help or are looking for cooperation feel free to contact us. [email protected]