no code implementations • 3 May 2024 • Sicong Liu, Wentao Zhou, Zimu Zhou, Bin Guo, Minfan Wang, Cheng Fang, Zheng Lin, Zhiwen Yu
There is a growing demand to deploy computation-intensive deep learning (DL) models on resource-constrained mobile devices for real-time intelligent applications.
no code implementations • 27 Sep 2023 • Sicong Liu, Bin Guo, Cheng Fang, Ziqi Wang, Shiyan Luo, Zimu Zhou, Zhiwen Yu
Accordingly, the accuracy and responsiveness of DL models are bounded by resource availability.
no code implementations • 13 Mar 2023 • Younan Mou, Sicong Liu
The underwater propagation environment for visible light signals is affected by complex factors such as absorption, shadowing, and reflection, making it very challengeable to achieve effective underwater visible light communication (UVLC) channel estimation.
no code implementations • 11 Mar 2023 • Tiankuo Wei, Sicong Liu
To improve the positioning accuracy and generalization capability in nonstationary environments, a cooperative VLP scheme based on federated learning (FL) is proposed in this paper.
no code implementations • 9 Mar 2023 • Xiao Tang, Sicong Liu
An RL-based algorithm is proposed to dynamically optimize the policy of power and interference control, maximizing the system utility in the complicated and dynamic environment.
no code implementations • 5 Mar 2023 • Xiao Tang, Sicong Liu, Xiaojiang Du, Mohsen Guizani
Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management.
no code implementations • 5 Mar 2023 • Xianyao Wang, Sicong Liu
Specifically, a CS-based framework is formulated exploiting the superposition of the received visible light signals at the multiple targets to be located via intertarget cooperation.
no code implementations • 29 Nov 2022 • Sicong Liu, Xiaochen Li, Zimu Zhou, Bin Guo, Meng Zhang, Haochen Shen, Zhiwen Yu
We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.
no code implementations • 16 Aug 2021 • Jiahui Cheng, Bin Guo, Jiaqi Liu, Sicong Liu, Guangzhi Wu, Yueqi Sun, Zhiwen Yu
To solve the imbalanced distribution problem, in this paper we propose TL-SDD: a novel Transfer Learning-based method for Surface Defect Detection.
1 code implementation • 13 Aug 2021 • Lei Ding, Haitao Guo, Sicong Liu, Lichao Mou, Jing Zhang, Lorenzo Bruzzone
Recent studies indicate that the SCD can be modeled through a triple-branch Convolutional Neural Network (CNN), which contains two temporal branches and a change branch.
no code implementations • 28 Jan 2021 • Sicong Liu, Bin Guo, Ke Ma, Zhiwen Yu, Junzhao Du
There are many deep learning (e. g., DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives.
no code implementations • 8 Jun 2020 • Sicong Liu, Junzhao Du, Kaiming Nan, ZimuZhou, Atlas Wang, Yingyan Lin
Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms.
no code implementations • 8 Jun 2020 • Sicong Liu, Junzhao Du, Anshumali Shrivastava, Lin Zhong
This work departs from prior works in methodology: we leverage adversarial learning to a better balance between privacy and utility.
no code implementations • 30 Sep 2019 • Zhecheng An, Sicong Liu
We propose a multi-class extension to the Wasserstein GAN, which allows our generative model to learn from both positive and negative samples.
no code implementations • ICLR 2019 • Sicong Liu, Anshumali Shrivastava, Junzhao Du, Lin Zhong
This work represents a methodical departure from prior works: we balance between a measure of privacy and another of utility by leveraging adversarial learning to find a sweeter tradeoff.