Paramixer

About

The PyTorch implementation of Paramixer from the paper Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention.

Citation

@inproceedings{9878955,
  title     = {Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention}, 
  author    = {Yu, Tong and Khalitov, Ruslan and Cheng, Lei and Yang, Zhirong},
  booktitle = {2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  year      = {2022},
  pages     = {681--690}
}

Datasets

1. LRA: https://mega.nz/file/tBdAyCwA#AvMIYJrkLset-Xb9ruA7fK04zZ_Jx2p7rdwrVVaTckE

Training Steps

1. Create a data folder:

mkdir data

2. Download the dataset compressed archive

wget $URL

3. Decompress the dataset compressed archive and put the contents into the data folder

unzip $dataset.zip
mv $datast ./data/$datast

4. Run the main file

python $dataset_main.py --task="$task"

Requirements

To install requirements:

pip3 install -r requirements.txt