Search Results for author: Xiaowen Zhang

Found 13 papers, 7 papers with code

SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder

1 code implementation26 Mar 2024 Dihan Zheng, Yihang Zou, Xiaowen Zhang, Chenglong Bao

We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks.

Image Denoising Image Restoration +2

Epsilon*: Privacy Metric for Machine Learning Models

no code implementations21 Jul 2023 Diana M. Negoescu, Humberto Gonzalez, Saad Eddin Al Orjany, Jilei Yang, Yuliia Lut, Rahul Tandra, Xiaowen Zhang, Xinyi Zheng, Zach Douglas, Vidita Nolkha, Parvez Ahammad, Gennady Samorodnitsky

We introduce Epsilon*, a new privacy metric for measuring the privacy risk of a single model instance prior to, during, or after deployment of privacy mitigation strategies.

Inference Attack Membership Inference Attack

EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems

1 code implementation26 Sep 2022 Mengli Cheng, Yue Gao, Guoqiang Liu, Hongsheng Jin, Xiaowen Zhang

We present EasyRec, an easy-to-use, extendable and efficient recommendation framework for building industrial recommendation systems.

feature selection Recommendation Systems

Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach

1 code implementation21 Apr 2022 Dihan Zheng, Xiaowen Zhang, Kaisheng Ma, Chenglong Bao

Current approaches aim at generating synthesized training data from unpaired samples by exploring the relationship between the corrupted and clean data.

Image Denoising Image Restoration +3

Learning From Unpaired Data: A Variational Bayes Approach

no code implementations29 Sep 2021 Dihan Zheng, Xiaowen Zhang, Kaisheng Ma, Chenglong Bao

Collecting the paired training data is a difficult task in practice, but the unpaired samples broadly exist.

Image Denoising Super-Resolution +1

Cross-domain error minimization for unsupervised domain adaptation

1 code implementation29 Jun 2021 Yuntao Du, Yinghao Chen, Fengli Cui, Xiaowen Zhang, Chongjun Wang

Unsupervised domain adaptation aims to transfer knowledge from a labeled source domain to an unlabeled target domain.

Unsupervised Domain Adaptation

An Unsupervised Deep Learning Approach for Real-World Image Denoising

1 code implementation ICLR 2021 Dihan Zheng, Sia Huat Tan, Xiaowen Zhang, Zuoqiang Shi, Kaisheng Ma, Chenglong Bao

In the real-world case, the noise distribution is so complex that the simplified additive white Gaussian (AWGN) assumption rarely holds, which significantly deteriorates the Gaussian denoisers' performance.

Decoder Image Denoising

Field-Tuned Quantum Effects in a Triangular-Lattice Ising Magnet

no code implementations18 Nov 2020 Yayuan Qin, Yao Shen, ChangLe Liu, Hongliang Wo, Yonghao Gao, Yu Feng, Xiaowen Zhang, Gaofeng Ding, Yiqing Gu, Qisi Wang, Shoudong Shen, Helen C. Walker, Robert Bewley, Jianhui Xu, Martin Boehm, Paul Steffens, Seiko Ohira-Kawamura, Naoki Murai, Astrid Schneidewind, Xin Tong, Gang Chen, Jun Zhao

We report thermodynamic and neutron scattering measurements of the triangular-lattice quantum Ising magnet TmMgGaO 4 in longitudinal magnetic fields.

Strongly Correlated Electrons Materials Science

Learning transferable and discriminative features for unsupervised domain adaptation

no code implementations26 Mar 2020 Yuntao Du, Ruiting Zhang, Xiaowen Zhang, Yirong Yao, Hengyang Lu, Chongjun Wang

In this paper, a novel method called \textit{learning TransFerable and Discriminative Features for unsupervised domain adaptation} (TFDF) is proposed to optimize these two objectives simultaneously.

Unsupervised Domain Adaptation

Dual Adversarial Domain Adaptation

1 code implementation1 Jan 2020 Yuntao Du, Zhiwen Tan, Qian Chen, Xiaowen Zhang, Yirong Yao, Chongjun Wang

Recent experiments have shown that when the discriminator is provided with domain information in both domains and label information in the source domain, it is able to preserve the complex multimodal information and high semantic information in both domains.


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