INSET

This repo contains PyTorch implementation of the paper "Enhancing Neural Subset Selection: Integrating Background Information Into Set Representations.".

Installation

Please ensure that:

• Python >= 3.7
• PyTorch >= 1.8.0

Experiments

Product Recommendation

To run on the Amazon baby registry dataset

python main.py equivset --train --cuda --symmetry --data_name amazon --amazon_cat <category_name>

category_name is chosen in ['toys', 'furniture', 'gear', 'carseats', 'bath', 'health', 'diaper', 'bedding', 'safety', 'feeding', 'apparel', 'media'].

Set Anomaly Detection

To run on the CelebA dataset

python main.py equivset --train --cuda --symmetry --data_name celeba

Reference Code

Our code is built upon the framework provided by https://github.com/SubsetSelection/EquiVSet/tree/main.

Citation

If you find our paper and repo useful, please consider to cite our paper:

@inproceedings{
xie2024enhancing,
title={Enhancing Neural Subset Selection: Integrating Background Information into Set Representations},
author={Binghui Xie and Yatao Bian and Kaiwen Zhou and Yongqiang Chen and Peilin Zhao and Bo Han and Wei Meng and James Cheng},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=eepoE7iLpL}
}