21 citations · 54 across the 15 of their papers we have counts for
3 papers · 1 filter
AE-StyleGAN: Improved Training of Style-Based Auto-Encoders
Ligong Han, Sri Harsha Musunuri, Martin Renqiang Min +3
StyleGANs have shown impressive results on data generation and manipulation in recent years, thanks to its disentangled style latent space. A lot of efforts have been made in inver…
Dual Projection Generative Adversarial Networks for Conditional Image Generation
Ligong Han, Martin Renqiang Min, Anastasis Stathopoulos +4
Conditional Generative Adversarial Networks (cGANs) extend the standard unconditional GAN framework to learning joint data-label distributions from samples, and have been establish…
Disentangled Recurrent Wasserstein Autoencoder
Jun Han, Martin Renqiang Min, Ligong Han +2
Learning disentangled representations leads to interpretable models and facilitates data generation with style transfer, which has been extensively studied on static data such as i…