This repository contains image downscaling & super-resolution project code based on the paper "Once-for-All: Train One Network and Specialize it for Efficient Deployment" (ICLR 2020).
The objectives of this proejct are
- Find the best image downscaling & super-resolution neural network architecture on mobile devices
- Support both 2x, 4x super-resolution in a single architecture.
@inproceedings{
cai2020once,
title={Once for All: Train One Network and Specialize it for Efficient Deployment},
author={Han Cai and Chuang Gan and Tianzhe Wang and Zhekai Zhang and Song Han},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://arxiv.org/pdf/1908.09791.pdf}
}
@inproceedings{
kim2018tar,
title={Task-Aware Image Downscaling},
author={Heewon Kim and Myungsub Choi and Bee Lim and Kyoung Mu Lee},
booktitle={European Conference on Computer Vision},
year={2018},
url={https://openaccess.thecvf.com/content_ECCV_2018/papers/Heewon_Kim_Task-Aware_Image_Downscaling_ECCV_2018_paper.pdf}
}
- Kernel Size
- Network Depth
- Expand Ratio
- Number of Pixelshuffle
"CAR: Learned Image Downscaling for Upscaling using Content Adaptive Resampler"
Dataset | Ours | CAR |
---|---|---|
Set14-2xUP | 39.15 | 35.61 |
Set14-4xUP | 31.01 | 30.30 |