no code implementations • 25 Mar 2024 • Xiaoyan Kui, Haonan Yan, Qinsong Li, Liming Chen, Beiji Zou
In this paper, we present a novel architecture named ChebMixer, a newly graph MLP Mixer that uses fast Chebyshev polynomials-based spectral filtering to extract a sequence of tokens.
no code implementations • 13 Oct 2023 • Qian Chen, Zilong Wang, Jiaqi Hu, Haonan Yan, Jianying Zhou, Xiaodong Lin
Federated learning (FL) is becoming a major driving force behind machine learning as a service, where customers (clients) collaboratively benefit from shared local updates under the orchestration of the service provider (server).
1 code implementation • CVPR 2023 • Ziyao Guo, Haonan Yan, Hui Li, Xiaodong Lin
Previous knowledge distillation methods have shown their impressive performance on model compression tasks, however, it is hard to explain how the knowledge they transferred helps to improve the performance of the student network.
no code implementations • CVPR 2022 • Chaojie Yang, Hanhui Li, Shengjie Wu, Shengkai Zhang, Haonan Yan, Nianhong Jiao, Jie Tang, Runnan Zhou, Xiaodan Liang, Tianxiang Zheng
This is because current methods mainly rely on a single pose/appearance model, which is limited in disentangling various poses and appearance in human images.
1 code implementation • ICCV 2021 • Haonan Yan, Jiaqi Chen, Xujie Zhang, Shengkai Zhang, Nianhong Jiao, Xiaodan Liang, Tianxiang Zheng
However, the popular DensePose-COCO dataset relies on a sophisticated manual annotation system, leading to severe limitations in acquiring the denser and more accurate annotated pose resources.
no code implementations • 1 Aug 2021 • Zhenyu Xie, Xujie Zhang, Fuwei Zhao, Haoye Dong, Michael C. Kampffmeyer, Haonan Yan, Xiaodan Liang
Despite recent progress on image-based virtual try-on, current methods are constraint by shared warping networks and thus fail to synthesize natural try-on results when faced with clothing categories that require different warping operations.
no code implementations • 1 Nov 2020 • Haonan Yan, Xiaoguang Li, Hui Li, Jiamin Li, Wenhai Sun, Fenghua Li
In MDP, we first propose a novel real-time model extraction status assessment scheme called Monitor to evaluate the situation of the model.
no code implementations • 6 Feb 2020 • Xiaoguang Li, Hui Li, Haonan Yan, Zelei Cheng, Wenhai Sun, Hui Zhu
Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries.