SchurNet

Overview

This repo contains code for the experimental part of the paper "Revisiting Multi-Permutation Equivariance Thourgh The Lens of Irreducible Representations".

Wasserstein Distance Computation

To install the necessary dependencies for this component, create the environment using the provided environment.yml file by running the following commands:

conda env create -f dependencies.yml
conda activate weis_dis

Data

Download the datasets from Here. Unzip the downloaded file and place the data in the Wasserstein_Distance folder.

Training

Choose one of the datasets of ncircle3, ncircle6, random, mn_small(modelnet small), mn_large(modelnet large), rna(RNA) named set_name. After downloading the data, please run the following command to start the training process:

cd Wasserstein_Distance/script
python train_wd.py --dataset_name set_name

Deep Weight Space Alignment

Installation

conda create --name deep-align python=3.9
conda activate deep-align
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.7 -c pytorch -c nvidia

cd DWS
pip install -r requirements.txt

Data

Choose set_type to be mnist or cifar.

MNIST MLP

To run the MLP experiments, first download the data:

mkdir -p datasets
wget "https://www.dropbox.com/s/sv85hrjswaspok4/mnist_classifiers.zip" -P data/datasets
unzip -q data/datasets/mnist_classifiers.zip -d data/datasets/samples

CIFAR10 MLP

To run the MLP experiments, first download the data:

mkdir -p datasets
wget "https://www.dropbox.com/s/sv85hrjswaspok4/cifar_classifiers.zip" -P data/datasets
unzip -q data/datasets/cifar_classifiers.zip -d data/datasets/samples

Split data:

Run

python code/experiments/utils/data_utils/generate_splits.py --set_type set_type

Training

For our model with the shared layers run:

python code/experiments/mlp_image_classifier/trainer.py --set_type set_type --shared True

For the baseline of Siamese model, run:

python code/experiments/mlp_image_classifier/trainer.py --set_type set_type --shared False

Citation

@misc{sverdlov2024revisiting,
      title={Revisiting Multi-Permutation Equivariance through the Lens of Irreducible Representations}, 
      author={Yonatan Sverdlov and Ido Springer and Nadav Dym},
      year={2024},
      eprint={2410.06665},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2410.06665}, 
}