Search Results for author: Minyoung Huh

Found 11 papers, 5 papers with code

Fighting Fake News: Image Splice Detection via Learned Self-Consistency

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.

Image Forensics

Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks

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.

Image-to-Image Translation Translation

The Low-Rank Simplicity Bias in Deep Networks

1 code implementation18 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.

Image Classification

Totems: Physical Objects for Verifying Visual Integrity

no code implementations26 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.

Image Forensics

Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks

no code implementations15 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.

Image Classification Quantization

Training Neural Networks from Scratch with Parallel Low-Rank Adapters

no code implementations26 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.

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