Distributed Generative Adversarial Net

2019/11/19 by Xiaoyu Wang, Ye Deng, Wang, Xiaoyu +3
Computer Science · #Digital Media Forensic Detection #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image Processing and 3D Reconstruction #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.1911.08128

openalex publication_date 2019/11/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Recently the Generative Adversarial Network has become a hot topic. Considering the application of GAN in multi-user environment, we propose Distributed-GAN. It enables multiple users to train with their own data locally and generates more diverse samples. Users don't need to share data with each other to avoid the leakage of privacy. In recent years, commercial companies have launched cloud platforms based on artificial intelligence to provide model for users who lack computing power. We hope our work can inspire these companies to provide more powerful AI services.

Citations

• Learning to Discover Cross-Domain Relations with Generative Adversarial Networks

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