2.3k citations · 6.3k across the 28 of their papers we have counts for
8 papers · 1 filter
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
David Bau, Jun-Yan Zhu, Hendrik Strobelt +4
Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample qu…
Visual Object Networks: Image Generation with Disentangled 3D Representation
Jun-Yan Zhu, Zhoutong Zhang, Chengkai Zhang +4
Recent progress in deep generative models has led to tremendous breakthroughs in image generation. However, while existing models can synthesize photorealistic images, they lack an…
Dataset Distillation
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba +1
Model distillation aims to distill the knowledge of a complex model into a simpler one. In this paper, we consider an alternative formulation called dataset distillation: we keep t…
Propagation Networks for Model-Based Control Under Partial Observation
Yunzhu Li, Jiajun Wu, Jun-Yan Zhu +3
There has been an increasing interest in learning dynamics simulators for model-based control. Compared with off-the-shelf physics engines, a learnable simulator can quickly adapt…
3D-Aware Scene Manipulation via Inverse Graphics
Shunyu Yao, Tzu Ming Harry Hsu, Jun-Yan Zhu +4
We aim to obtain an interpretable, expressive, and disentangled scene representation that contains comprehensive structural and textural information for each object. Previous scene…
Video-to-Video Synthesis
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu +4
We study the problem of video-to-video synthesis, whose goal is to learn a mapping function from an input source video (e.g., a sequence of semantic segmentation masks) to an outpu…
Spatially Transformed Adversarial Examples
Chaowei Xiao, Jun-Yan Zhu, Bo Li +3
Recent studies show that widely used deep neural networks (DNNs) are vulnerable to carefully crafted adversarial examples. Many advanced algorithms have been proposed to generate a…
Generating Adversarial Examples with Adversarial Networks
Chaowei Xiao, Bo Li, Jun-Yan Zhu +3
Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mi…