1 code implementation • 30 Aug 2016 • Minyoung Huh, Pulkit Agrawal, Alexei A. Efros
Which is better: more classes or more examples per class?
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 • 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 Synthia Novel View Synthesis
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.
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.
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.
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.
no code implementations • 26 Sep 2022 • Jingwei Ma, Lucy Chai, Minyoung Huh, Tongzhou Wang, Ser-Nam Lim, Phillip Isola, Antonio Torralba
We introduce a new approach to image forensics: placing physical refractive objects, which we call totems, into a scene so as to protect any photograph taken of that scene.
no code implementations • 15 May 2023 • Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola
We identify the factors that contribute to this issue, including the codebook gradient sparsity and the asymmetric nature of the commitment loss, which leads to misaligned code-vector assignments.
no code implementations • 26 Feb 2024 • Minyoung Huh, Brian Cheung, Jeremy Bernstein, Phillip Isola, Pulkit Agrawal
The scalability of deep learning models is fundamentally limited by computing resources, memory, and communication.
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.