paper link: http://alisec-competition.oss-cn-shanghai.aliyuncs.com/competition_papers/20211201/rank10.pdf

This work is sponsored by Natural Science Foundation of China(62276242), CAAI-Huawei MindSpore Open Fund(CAAIXSJLJJ-2021-016B), Anhui Province Key Research and Development Program(202104a05020007), and USTC Research Funds of the Double First-Class Initiative(YD2350002001)”。

Later, Mindspore will be provided for implementation.

How to run

1. Obtain all Cifar10 data from the official website to the folder CIFAR-10-FANs-PY;
2. Create two empty folders. new_data and train_model;
3. Run gen_corr_data.ipynb to generate corruption data;
4. Run final_gen_data_iter to generate the first generation of the data;
5. Run train.py to generate the first generation model;
6. Generate the next generation of the data.
7. Loop 5 ~ 6 until num = 6. In general, it takes 5 times to train the model to get the final submitted model, and 4 times to get the final submitted data.npy.