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ORCIDMinyoung Huh
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2020 – today
- 2024
[c7]Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip Isola:
Position: The Platonic Representation Hypothesis. ICML 2024- [i9]Minyoung Huh, Brian Cheung, Jeremy Bernstein, Phillip Isola, Pulkit Agrawal:
Training Neural Networks from Scratch with Parallel Low-Rank Adapters. CoRR abs/2402.16828 (2024) - [i8]Minyoung Huh, Brian Cheung, Tongzhou Wang, Phillip Isola:
The Platonic Representation Hypothesis. CoRR abs/2405.07987 (2024) - [i7]Tim Large, Yang Liu, Minyoung Huh, Hyojin Bahng, Phillip Isola, Jeremy Bernstein:
Scalable Optimization in the Modular Norm. CoRR abs/2405.14813 (2024) - 2023
- [j1]Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola:
The Low-Rank Simplicity Bias in Deep Networks. Trans. Mach. Learn. Res. 2023 (2023) - [c6]Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola:
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. ICML 2023: 14096-14113 - [i6]Minyoung Huh, Brian Cheung, Pulkit Agrawal, Phillip Isola:
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks. CoRR abs/2305.08842 (2023) - 2022
- [c5]Jingwei Ma
, Lucy Chai, Minyoung Huh, Tongzhou Wang, Ser-Nam Lim, Phillip Isola, Antonio Torralba:
Totems: Physical Objects for Verifying Visual Integrity. ECCV (14) 2022: 164-180 - [i5]Jingwei Ma, Lucy Chai, Minyoung Huh, Tongzhou Wang, Ser-Nam Lim, Phillip Isola, Antonio Torralba:
Totems: Physical Objects for Verifying Visual Integrity. CoRR abs/2209.13032 (2022) - 2021
- [i4]Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola:
The Low-Rank Simplicity Bias in Deep Networks. CoRR abs/2103.10427 (2021) - [i3]Toru Lin, Minyoung Huh, Christopher Stauffer, Ser-Nam Lim, Phillip Isola:
Learning to Ground Multi-Agent Communication with Autoencoders. CoRR abs/2110.15349 (2021) - 2020
- [c4]Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, Aaron Hertzmann:
Transforming and Projecting Images into Class-Conditional Generative Networks. ECCV (2) 2020: 17-34 - [i2]Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, Aaron Hertzmann:
Transforming and Projecting Images into Class-conditional Generative Networks. CoRR abs/2005.01703 (2020)
2010 – 2019
- 2019
- [c3]Minyoung Huh, Shao-Hua Sun, Ning Zhang:
Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks. CVPR 2019: 1476-1485 - 2018
- [c2]Minyoung Huh, Andrew Liu, Andrew Owens, Alexei A. Efros:
Fighting Fake News: Image Splice Detection via Learned Self-Consistency. ECCV (11) 2018: 106-124 - [c1]Shao-Hua Sun, Minyoung Huh, Yuan-Hong Liao, Ning Zhang, Joseph J. Lim:
Multi-view to Novel View: Synthesizing Novel Views With Self-learned Confidence. ECCV (3) 2018: 162-178 - [i1]Minyoung Huh, Andrew Liu, Andrew Owens, Alexei A. Efros:
Fighting Fake News: Image Splice Detection via Learned Self-Consistency. CoRR abs/1805.04096 (2018)
Coauthor Index
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