no code implementations • 25 Dec 2023 • Ziyan Chen, Jing Cheng
PGDUDST requires only 58% of the training time of RDLUF-MixS^2-9stg to achieve comparable reconstruction results.
1 code implementation • 29 Aug 2023 • Xinqi Lin, Jingwen He, Ziyan Chen, Zhaoyang Lyu, Bo Dai, Fanghua Yu, Wanli Ouyang, Yu Qiao, Chao Dong
We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks in a unified framework.
Ranked #1 on Blind Face Restoration on LFW
1 code implementation • CVPR 2023 • Hao Huang, Ziyan Chen, Huanran Chen, Yongtao Wang, Kevin Zhang
Then, we analogize patch optimization with regular model optimization, proposing a series of self-ensemble approaches on the input data, the attacked model, and the adversarial patch to efficiently make use of the limited information and prevent the patch from overfitting.
no code implementations • 28 Jun 2022 • Ziyan Chen, Zhentao Liu, Chenyu Hu, Heng Wu, Jianrong Wu, Jinda Lin, Zhishen Tong, Hong Yu, Shensheng Han
When applying deep learning into GISC spectral camera, there are several challenges need to be solved: 1) how to deal with the large amount of 3D hyperspectral data, 2) how to reduce the influence caused by the uncertainty of the random reference measurements, 3) how to improve the reconstructed image quality as far as possible.
1 code implementation • 2 Jun 2022 • Ziyan Chen, Jiazhen Liu, Changwen Cao, Changlong Jin, Hakil Kim
In the proposed framework, the domain mapper is an approximation to a specific extraction function thus the training is only a one-time effort with limited data.
1 code implementation • 2 Sep 2020 • Jiuniu Wang, Wenjia Xu, Xingyu Fu, Yang Wei, Li Jin, Ziyan Chen, Guangluan Xu, Yirong Wu
This model enhances the question answering system in the multi-document scenario from three aspects: model structure, optimization goal, and training method, corresponding to Multilayer Attention (MA), Cross Evidence (CE), and Adversarial Training (AT) respectively.
no code implementations • 3 Sep 2018 • Jiuniu Wang, Xingyu Fu, Guangluan Xu, Yirong Wu, Ziyan Chen, Yang Wei, Li Jin
Meanwhile, we construct A3Net for the WebQA dataset.