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WaveCNet [paper1][paper2]

The WaveCNet is designed using DWT and the commonly used CNN networks in PyTorch: https://pytorch.org/docs/stable/torchvision/models.html#classification

The main.py is revised on the PyTorch image classification code: https://github.com/pytorch/examples/blob/42e5b996718797e45c46a25c55b031e6768f8440/imagenet/main.py#L89-L101

WaveUNet for image segmentation has been renamed as WaveSNet: https://github.com/LiQiufu/WaveSNet

(0) Paper Title

Wavelet Integrated CNNs for Noise-Robust Image Classification

(1) Training WaveCNet on ImageNet

 CUDA_VISIBLE_DEVICES=0 python main.py --data /PYTHON_TO_IMANGENET -a resnet18_dwt -b 256 -w bior3.3 --gpu 0 --lr 0.1

(2) The trained weight files

The pretrained weight files have be uploaded on the website: https://pan.baidu.com/s/1T8vrOd0Zq8jv9fT5b7jdcQ (passwords: auc9)

(3) The paper

If the code or method help you in the research, please cite the following paper:

@inproceedings{li2020wavelet,
  title={Wavelet integrated CNNs for noise-robust image classification},
  author={Li, Qiufu and Shen, Linlin and Guo, Sheng and Lai, Zhihui},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={7245--7254},
  year={2020}
}

@article(qiufu_2021_TIP,
author = {Li, Qiufu and Shen, Linlin and Guo, Sheng and Lai, Zhihui},
title = {WaveCNet: Wavelet Integrated CNNs to Suppress Aliasing Effect for Noise-Robust Image Classification},
booktitle = {IEEE Transactions on Image Processing (TIP)},
month = {july},
year = {2021},
pages = {7074--7089},
vol = {30}
}