This repository contains the caffe prototxt and trained model described in the paper "Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks".
For more details, please visit our project page: WAE project page.
224x224 center crop validation accuracy on ImageNet, evaluated with a C++ implementation on Intel i7 CPU (3.50GHz) and Nvidia GeForce GTX TITAN-X GPU.
| Top-1 | Top-5 | CPU (ms) | GPU (ms) |
|---|---|---|---|
| 67.88% | 88.27% | 411.63 | 2.37 |
Note: The model is retrained in the same way as desribed in the paper, and the accuracy is slightly better than that reported in the paper.
The trained model can be download from google drive or baidu cloud.
If you find this work useful for your research, please cite:
@inproceedings{{chen2018learning,
author = {Chen, Tianshui and Lin, Liang and Zuo, Wangmeng and Luo, Xiaonan and Zhang, Lei},
title = {Learning a Wavelet-like Auto-Encoder to Accelerate Deep Neural Networks},
booktitle = {AAAI},
year = {2018}
}
Feel free to contact me if you have any question (Tianshui Chen tianshuichen@gmail.com)