385 citations · 1.5k across the 36 of their papers we have counts for
5 papers · 1 filter
Understanding the Role of Individual Units in a Deep Neural Network
David Bau, Jun-Yan Zhu, Hendrik Strobelt +3
Deep neural networks excel at finding hierarchical representations that solve complex tasks over large data sets. How can we humans understand these learned representations? In thi…
What makes fake images detectable? Understanding properties that generalize
Lucy Chai, David Bau, Ser-Nam Lim +1
The quality of image generation and manipulation is reaching impressive levels, making it increasingly difficult for a human to distinguish between what is real and what is fake. H…
Rewriting a Deep Generative Model
David Bau, Steven Liu, Tongzhou Wang +2
A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are…
Diverse Image Generation via Self-Conditioned GANs
Steven Liu, Tongzhou Wang, David Bau +2
We introduce a simple but effective unsupervised method for generating realistic and diverse images. We train a class-conditional GAN model without using manually annotated class l…
Semantic Photo Manipulation with a Generative Image Prior
David Bau, Hendrik Strobelt, William Peebles +4
Despite the recent success of GANs in synthesizing images conditioned on inputs such as a user sketch, text, or semantic labels, manipulating the high-level attributes of an existi…