42 citations · 42 across the 3 of their papers we have counts for
3 papers
cs.CV2024
MCGAN: Enhancing GAN Training with Regression-Based Generator Loss
Baoren Xiao, Hao Ni, Weixin Yang
Generative adversarial networks (GANs) have emerged as a powerful tool for generating high-fidelity data. However, the main bottleneck of existing approaches is the lack of supervi…
cs.LG2021
Sig-Wasserstein GANs for Time Series Generation
Hao Ni, Lukasz Szpruch, Marc Sabate-Vidales +3
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines. In this work, we develop high-fidelity t…
cs.LG2020★ 42 cited
Conditional Sig-Wasserstein GANs for Time Series Generation
Shujian Liao, Hao Ni, Lukasz Szpruch +3
Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high dimensional probability measures. However, these methods struggle t…