no code implementations • ECCV 2020 • Shuo Wang, Yuexiang Li, Kai Ma, Ruhui Ma, Haibing Guan, Yefeng Zheng
In this paper, we investigate the overlapping problem of recent uncertainty-based approaches and propose to alleviate the issue by taking representativeness into consideration.
no code implementations • ECCV 2020 • Kunyuan Du, Ya zhang, Haibing Guan, Qi Tian, Shenggan Cheng, James Lin
Compared with low-bit models trained directly, the proposed framework brings 0. 5% to 3. 4% accuracy gains to three different quantization schemes.
no code implementations • 17 Mar 2024 • Xiaoyu Wu, Yang Hua, Chumeng Liang, Jiaru Zhang, Hao Wang, Tao Song, Haibing Guan
In response, we present Contrasting Gradient Inversion for Diffusion Models (CGI-DM), a novel method featuring vivid visual representations for digital copyright authentication.
no code implementations • 19 Dec 2023 • Peishen Yan, Hao Wang, Tao Song, Yang Hua, Ruhui Ma, Ningxin Hu, Mohammad R. Haghighat, Haibing Guan
Specifically, the FL server applies parameter-level masks to model updates uploaded by clients and trains the masks over a small clean dataset (i. e., root dataset) to learn the subtle difference between benign and malicious model updates in a high-dimension space.
1 code implementation • NeurIPS 2023 • Jianqing Zhang, Yang Hua, Jian Cao, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan
Recently, federated learning (FL) is popular for its privacy-preserving and collaborative learning abilities.
3 code implementations • ICCV 2023 • Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Jian Cao, Haibing Guan
Federated Learning (FL) is popular for its privacy-preserving and collaborative learning capabilities.
3 code implementations • 1 Jul 2023 • Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan
To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively.
1 code implementation • 9 Feb 2023 • Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan
Recently, Diffusion Models (DMs) boost a wave in AI for Art yet raise new copyright concerns, where infringers benefit from using unauthorized paintings to train DMs to generate novel paintings in a similar style.
2 code implementations • 2 Dec 2022 • Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan
A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client.
1 code implementation • CVPR 2021 • Jiaru Zhang, Yang Hua, Zhengui Xue, Tao Song, Chengyu Zheng, Ruhui Ma, Haibing Guan
Bayesian neural networks have been widely used in many applications because of the distinctive probabilistic representation framework.
1 code implementation • 25 May 2021 • Yanran Wu, Xiangtai Li, Chen Shi, Yunhai Tong, Yang Hua, Tao Song, Ruhui Ma, Haibing Guan
Motivated by this, we propose a novel network by aligning two-path information into each other through a learned flow field.
1 code implementation • ICCV 2021 • Yuxin Ma, Yang Hua, Hanming Deng, Tao Song, Hao Wang, Zhengui Xue, Heng Cao, Ruhui Ma, Haibing Guan
Vessel segmentation is critically essential for diagnosinga series of diseases, e. g., coronary artery disease and retinal disease.
no code implementations • 11 Apr 2020 • Kunyuan Du, Ya zhang, Haibing Guan
This paper proposes Quantizable DNNs, a special type of DNNs that can flexibly quantize its bit-width (denoted as `bit modes' thereafter) during execution without further re-training.
no code implementations • 27 Apr 2019 • Jinkun Cao, Jinhao Zhu, Liwei Lin, Zhengui Xue, Ruhui Ma, Haibing Guan
To avoid privacy leaks, outsourced data usually is encrypted before being sent to cloud servers, which disables traditional search schemes for plain text.