Jun-Yan Zhu

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MIT ∙ berkeley college

Postdoctoral Researcher at Massachusetts Institute of Technology

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Understanding the Role of Individual Units in a Deep Neural Network

Deep neural networks excel at finding hierarchical representations that ...
09/10/2020 ∙ by David Bau, et al. ∙ 11 ∙ share

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The Hessian Penalty: A Weak Prior for Unsupervised Disentanglement

Existing disentanglement methods for deep generative models rely on hand...
08/24/2020 ∙ by William Peebles, et al. ∙ 9 ∙ share

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Contrastive Learning for Unpaired Image-to-Image Translation

In image-to-image translation, each patch in the output should reflect t...
07/30/2020 ∙ by Taesung Park, et al. ∙ 1 ∙ share

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Rewriting a Deep Generative Model

A deep generative model such as a GAN learns to model a rich set of sema...
07/30/2020 ∙ by David Bau, et al. ∙ 12 ∙ share

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Swapping Autoencoder for Deep Image Manipulation

Deep generative models have become increasingly effective at producing r...
07/01/2020 ∙ by Taesung Park, et al. ∙ 2 ∙ share

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Differentiable Augmentation for Data-Efficient GAN Training

The performance of generative adversarial networks (GANs) heavily deteri...
06/18/2020 ∙ by Shengyu Zhao, et al. ∙ 11 ∙ share

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Diverse Image Generation via Self-Conditioned GANs

We introduce a simple but effective unsupervised method for generating r...
06/18/2020 ∙ by Steven Liu, et al. ∙ 12 ∙ share

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Semantic Photo Manipulation with a Generative Image Prior

Despite the recent success of GANs in synthesizing images conditioned on...
05/15/2020 ∙ by David Bau, et al. ∙ 21 ∙ share

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Transforming and Projecting Images into Class-conditional Generative Networks

We present a method for projecting an input image into the space of a cl...
05/04/2020 ∙ by Minyoung Huh, et al. ∙ 1 ∙ share

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State of the Art on Neural Rendering

Efficient rendering of photo-realistic virtual worlds is a long standing...
04/08/2020 ∙ by Ayush Tewari, et al. ∙ 13 ∙ share

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GAN Compression: Efficient Architectures for Interactive Conditional GANs

Conditional Generative Adversarial Networks (cGANs) have enabled control...
03/19/2020 ∙ by Muyang Li, et al. ∙ 69 ∙ share

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Seeing What a GAN Cannot Generate

Despite the success of Generative Adversarial Networks (GANs), mode coll...
10/24/2019 ∙ by David Bau, et al. ∙ 53 ∙ share

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Connecting Touch and Vision via Cross-Modal Prediction

Humans perceive the world using multi-modal sensory inputs such as visio...
06/14/2019 ∙ by Yunzhu Li, et al. ∙ 4 ∙ share

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Semantic Image Synthesis with Spatially-Adaptive Normalization

We propose spatially-adaptive normalization, a simple but effective laye...
03/18/2019 ∙ by Taesung Park, et al. ∙ 62 ∙ share

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Visualizing and Understanding Generative Adversarial Networks (Extended Abstract)

Generative Adversarial Networks (GANs) have achieved impressive results ...
01/29/2019 ∙ by David Bau, et al. ∙ 22 ∙ share

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Visual Object Networks: Image Generation with Disentangled 3D Representation

Recent progress in deep generative models has led to tremendous breakthr...
12/06/2018 ∙ by Jun-Yan Zhu, et al. ∙ 8 ∙ share

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Dataset Distillation

Model distillation aims to distill the knowledge of a complex model into...
11/27/2018 ∙ by Tongzhou Wang, et al. ∙ 3 ∙ share

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GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

Generative Adversarial Networks (GANs) have recently achieved impressive...
11/26/2018 ∙ by David Bau, et al. ∙ 10 ∙ share

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Propagation Networks for Model-Based Control Under Partial Observation

There has been an increasing interest in learning dynamics simulators fo...
09/28/2018 ∙ by Yunzhu Li, et al. ∙ 4 ∙ share

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3D-Aware Scene Manipulation via Inverse Graphics

We aim to obtain an interpretable, expressive and disentangled scene rep...
08/28/2018 ∙ by Shunyu Yao, et al. ∙ 0 ∙ share

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Video-to-Video Synthesis

We study the problem of video-to-video synthesis, whose goal is to learn...
08/20/2018 ∙ by Ting-Chun Wang, et al. ∙ 4 ∙ share

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Spatially transformed adversarial examples

Recent studies show that widely used deep neural networks (DNNs) are vul...
01/08/2018 ∙ by Chaowei Xiao, et al. ∙ 0 ∙ share

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Generating adversarial examples with adversarial networks

Deep neural networks (DNNs) have been found to be vulnerable to adversar...
01/08/2018 ∙ by Chaowei Xiao, et al. ∙ 0 ∙ share

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Toward Multimodal Image-to-Image Translation

Many image-to-image translation problems are ambiguous, as a single inpu...
11/30/2017 ∙ by Jun-Yan Zhu, et al. ∙ 0 ∙ share

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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

We present a new method for synthesizing high-resolution photo-realistic...
11/30/2017 ∙ by Ting-Chun Wang, et al. ∙ 0 ∙ share

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CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Domain adaptation is critical for success in new, unseen environments. A...
11/08/2017 ∙ by Judy Hoffman, et al. ∙ 0 ∙ share

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Real-Time User-Guided Image Colorization with Learned Deep Priors

We propose a deep learning approach for user-guided image colorization. ...
05/08/2017 ∙ by Richard Zhang, et al. ∙ 0 ∙ share

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Light Field Video Capture Using a Learning-Based Hybrid Imaging System

Light field cameras have many advantages over traditional cameras, as th...
05/08/2017 ∙ by Ting-Chun Wang, et al. ∙ 0 ∙ share

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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

Image-to-image translation is a class of vision and graphics problems wh...
03/30/2017 ∙ by Jun-Yan Zhu, et al. ∙ 0 ∙ share

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Image-to-Image Translation with Conditional Adversarial Networks

We investigate conditional adversarial networks as a general-purpose sol...
11/21/2016 ∙ by Phillip Isola, et al. ∙ 0 ∙ share

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Generative Visual Manipulation on the Natural Image Manifold

Realistic image manipulation is challenging because it requires modifyin...
09/12/2016 ∙ by Jun-Yan Zhu, et al. ∙ 0 ∙ share

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A 4D Light-Field Dataset and CNN Architectures for Material Recognition

We introduce a new light-field dataset of materials, and take advantage ...
08/24/2016 ∙ by Ting-Chun Wang, et al. ∙ 0 ∙ share

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