Max-Matching

AAAA'21: Learning with Group Noise (Pytorch implementation).

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This is the code for the paper:

Learning with Group Noise

Qizhou Wang*, Jiangchao Yao*, Chen Gong, Tongliang Liu, Mingming Gong, Hongxia Yang, Bo Han.

To be presented at AAAI 2021.

If you find this code useful in your research then please cite

@inproceedings{wang2021maxmatching,
title={Learning with Group Noise},
author={Qizhou Wang and Jiangchao Yao and Chen Gong and Tongliang Liu and Mingming Gong and Hongxia Yang and Bo Han},
booktitle={AAAI},
pages={10192--10200},
year={2021}
}

Setups

All code was developed and tested on a single machine equiped with a NVIDIA GTX3090 GPU. The environment is as bellow:

• Window 10

• CUDA 10.2.89

• Python 3.7.6 (Anaconda 4.9.2 64 bit)

• PyTorch 1.5.0

• numpy 1.18.1

Running Max-Matching on benchmark datasets from Amazon

Here is an example:

python main.py --dataset=Video

Performance

| Dataset  | Video | Video | Beauty | Beauty | Game  | Game  |
|----------|-------|-------|--------|--------|-------|-------|
| Metric   | HIT   | NDCG  | HIT    | NDCG   | HIT   | NDCG  |
| Accuracy | 0.694 | 0.473 | 0.561  | 0.389  | 0.518 | 0.345 |

We will release the improved realizations and other applications in the upcoming journal verison.

Contact: Qizhou Wang (qizhouwang.nanjing@gmail.com).