1.2k citations · 1.8k across the 51 of their papers we have counts for
6 papers · 1 filter
Image Processing Using Multi-Code GAN Prior
Jinjin Gu, Yujun Shen, Bolei Zhou
Despite the success of Generative Adversarial Networks (GANs) in image synthesis, applying trained GAN models to real image processing remains challenging. Previous methods typical…
Interpreting the Latent Space of GANs for Semantic Face Editing
Yujun Shen, Jinjin Gu, Xiaoou Tang +1
Despite the recent advance of Generative Adversarial Networks (GANs) in high-fidelity image synthesis, there lacks enough understanding of how GANs are able to map a latent code sa…
Suppressing Model Overfitting for Image Super-Resolution Networks
Ruicheng Feng, Jinjin Gu, Yu Qiao +1
Large deep networks have demonstrated competitive performance in single image super-resolution (SISR), with a huge volume of data involved. However, in real-world scenarios, due to…
Blind Super-Resolution With Iterative Kernel Correction
Jinjin Gu, Hannan Lu, Wangmeng Zuo +1
Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume th…
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
Guocheng Qian, Yuanhao Wang, Jinjin Gu +4
Imaging is usually a mixture problem of incomplete color sampling, noise degradation, and limited resolution. This mixture problem is typically solved by a sequential solution that…
Two-phase Hair Image Synthesis by Self-Enhancing Generative Model
Haonan Qiu, Chuan Wang, Hang Zhu +3
Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GA…