55 lines
No EOL
2 KiB
Markdown
55 lines
No EOL
2 KiB
Markdown
<div align="center">
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<h1> DisMouse: Disentangling Information from Mouse Movement Data </h1>
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**[Guanhua Zhang][4], [Zhiming Hu][5], [Andreas Bulling][6]** <br>
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**ACM UIST 2024**, Pittsburgh, USA <br>
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**[[Project][2]]** **[[Paper][7]]** </div>
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----------------
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# Virtual environment setup
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We recommend to setup a virtual environment using Anaconda. <br>
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1. Create a conda environment and install dependencies
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```shell
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conda env create --name dismouse --file=environment.yaml
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conda activate dismouse
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```
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2. Clone our repository to download our code
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```shell
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git clone this_repo.git
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```
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# Run the code to train DisMouse
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Our code supports training using GPUs. You can also assign a particular card via CUDA_VISIBLE_DEVICES (e.g., the following commands use GPU card no.3). Simply execute
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```shell
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CUDA_VISIBLE_DEVICES=3 python main.py
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```
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## Use your own configurations
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Change parameters in ```templates.py -> mouse_autoenc``` and ```config.py -> TrainConfig```
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## Train on your mouse dataset
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Plug in your data loader function in ```dataset.py -> loadDataset``` (line 6)
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# Citation
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If you find our code useful or use it in your own projects, please cite our paper:
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```
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@inproceedings{zhang24_uist,
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title = {DisMouse: Disentangling Information from Mouse Movement Data},
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author = {Zhang, Guanhua and Hu, Zhiming and Bulling, Andreas},
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year = {2024},
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pages = {1--13},
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booktitle = {Proc. ACM Symposium on User Interface Software and Technology (UIST)},
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doi = {10.1145/3654777.3676411}
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}
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```
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# Acknowledgements
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Our work is built on the codebase of [Diffusion Autoencoders][1]. Thanks to the authors for sharing their code.
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[1]: https://diff-ae.github.io/
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[2]: https://perceptualui.org/publications/zhang24_uist/
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[4]: https://scholar.google.com/citations?user=NqkK0GwAAAAJ&hl=en
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[5]: https://scholar.google.com/citations?hl=en&user=OLB_xBEAAAAJ
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[6]: https://www.perceptualui.org/people/bulling/
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[7]: https://perceptualui.org/publications/zhang24_uist.pdf |