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StochNorm

Original implementation for NeurIPS 2020 paper Stochastic Normalization.

[News] 2022/04/27 Please refer to the Transfer Learning Library for a modular implementation.

<p align="center"> <img src="https://github.com/thuml/StochNorm/blob/main/arch.png" width="300"> <br/> <br/> <a href="https://github.com/thuml/StochNorm/blob/main/LICENSE"><img alt="License" src="https://img.shields.io/github/license/thuml/StochNorm?" /></a> </p>

Dependencies

Datasets

DatasetDownload Link
CUB-200-2011http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
Stanford Carshttp://ai.stanford.edu/~jkrause/cars/car_dataset.html
FGVC Aircrafthttp://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/
NIH Chest X-rayhttps://nihcc.app.box.com/v/ChestXray-NIHCC

Quick Start

python --gpu [gpu_num] --data_path /path/to/dataset --class_num [class_num] --p 0.5 train.py 

Some Results

We re-trained our StochNorm with this code on full 15% train data of CUB-200-2011. Results are shown in the table below.

Sampling RateTop-1 Acc(%)
15%50.41
30%62.09
50%72.05
100%79.65

Citation

If you use this code for your research, please consider citing:

@article{kou2020stochastic,
  title={Stochastic Normalization},
  author={Kou, Zhi and You, Kaichao and Long, Mingsheng and Wang, Jianmin},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

Contact

If you have any problem about our code, feel free to contact kz19@mails.tsinghua.edu.cn.