no code implementations • 15 Mar 2024 • Zixiao Wang, Yunheng Shen, Xufeng Yao, Wenqian Zhao, Yang Bai, Farzan Farnia, Bei Yu
Existing works focus on fixed-size layout pattern generation, while the more practical free-size pattern generation receives limited attention.
no code implementations • 18 Oct 2023 • Zixiao Wang, Farzan Farnia, Zhenghao Lin, Yunheng Shen, Bei Yu
First, we focus on the Fr\'echet inception distance (FID) and consider the following FID-based aggregate scores over the clients: 1) FID-avg as the mean of clients' individual FID scores, 2) FID-all as the FID distance of the trained model to the collective dataset containing all clients' data.
no code implementations • 30 Sep 2023 • Xiang Liu, Liangxi Liu, Feiyang Ye, Yunheng Shen, Xia Li, Linshan Jiang, Jialin Li
Efficiently aggregating trained neural networks from local clients into a global model on a server is a widely researched topic in federated learning.
no code implementations • 11 Apr 2023 • Yunheng Shen, Haoxiang Wang, Hairong Lv
Federated learning aims to learn a global model collaboratively while the training data belongs to different clients and is not allowed to be exchanged.
no code implementations • 23 Mar 2023 • Zixiao Wang, Yunheng Shen, Wenqian Zhao, Yang Bai, Guojin Chen, Farzan Farnia, Bei Yu
Deep generative models dominate the existing literature in layout pattern generation.