349 citations · 627 across the 25 of their papers we have counts for
5 papers · 1 filter
Collaging Class-specific GANs for Semantic Image Synthesis
Yuheng Li, Yijun Li, Jingwan Lu +3
We propose a new approach for high resolution semantic image synthesis. It consists of one base image generator and multiple class-specific generators. The base generator generates…
Few-shot Image Generation via Cross-domain Correspondence
Utkarsh Ojha, Yijun Li, Jingwan Lu +4
Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large sourc…
IMAGINE: Image Synthesis by Image-Guided Model Inversion
Pei Wang, Yijun Li, Krishna Kumar Singh +2
We introduce an inversion based method, denoted as IMAge-Guided model INvErsion (IMAGINE), to generate high-quality and diverse images from only a single training sample. We levera…
Rethinking and Improving the Robustness of Image Style Transfer
Pei Wang, Yijun Li, Nuno Vasconcelos
Extensive research in neural style transfer methods has shown that the correlation between features extracted by a pre-trained VGG network has a remarkable ability to capture the v…
Content-Aware GAN Compression
Yuchen Liu, Zhixin Shu, Yijun Li +3
Generative adversarial networks (GANs), e.g., StyleGAN2, play a vital role in various image generation and synthesis tasks, yet their notoriously high computational cost hinders th…