87 citations · 719 across the 60 of their papers we have counts for
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Improving the Fairness of Deep Generative Models without Retraining
Shuhan Tan, Yujun Shen, Bolei Zhou
Generative Adversarial Networks (GANs) advance face synthesis through learning the underlying distribution of observed data. Despite the high-quality generated faces, some minority…
Generative Hierarchical Features from Synthesizing Images
Yinghao Xu, Yujun Shen, Jiapeng Zhu +2
Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data. However, how the features learned from s…
Closed-Form Factorization of Latent Semantics in GANs
Yujun Shen, Bolei Zhou
A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In order to ident…
InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs
Yujun Shen, Ceyuan Yang, Xiaoou Tang +1
Although Generative Adversarial Networks (GANs) have made significant progress in face synthesis, there lacks enough understanding of what GANs have learned in the latent represent…
In-Domain GAN Inversion for Real Image Editing
Jiapeng Zhu, Yujun Shen, Deli Zhao +1
Recent work has shown that a variety of semantics emerge in the latent space of Generative Adversarial Networks (GANs) when being trained to synthesize images. However, it is diffi…
Residual Knowledge Distillation
Mengya Gao, Yujun Shen, Quanquan Li +1
Knowledge distillation (KD) is one of the most potent ways for model compression. The key idea is to transfer the knowledge from a deep teacher model (T) to a shallower student (S)…