no code implementations • ECCV 2020 • Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, Aaron Hertzmann
We present a method for projecting an input image into the space of a class-conditional generative neural network.
no code implementations • NeurIPS 2021 • Toru Lin, Minyoung Huh, Chris Stauffer, Ser-Nam Lim, Phillip Isola
Communication requires having a common language, a lingua franca, between agents.
1 code implementation • 18 Mar 2021 • Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola
We show empirically that our claim holds true on finite width linear and non-linear models on practical learning paradigms and show that on natural data, these are often the solutions that generalize well.
2 code implementations • 4 May 2020 • Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, Aaron Hertzmann
We present a method for projecting an input image into the space of a class-conditional generative neural network.
no code implementations • CVPR 2019 • Minyoung Huh, Shao-Hua Sun, Ning Zhang
We propose feedback adversarial learning (FAL) framework that can improve existing generative adversarial networks by leveraging spatial feedback from the discriminator.
1 code implementation • Proceedings of the 15th European Conference on Computer Vision, 2018 • Shao-Hua Sun, Minyoung Huh, Yuan-Hong Liao, Ning Zhang, Joseph J. Lim
We address the task of multi-view novel view synthesis, where we are interested in synthesizing a target image with an arbitrary camera pose from given source images.
Ranked #1 on
Novel View Synthesis
on KITTI Novel View Synthesis
3 code implementations • ECCV 2018 • Minyoung Huh, Andrew Liu, Andrew Owens, Alexei A. Efros
In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs.
1 code implementation • 30 Aug 2016 • Minyoung Huh, Pulkit Agrawal, Alexei A. Efros
Which is better: more classes or more examples per class?