Search Results for author: Jun-Hyuk Kim

Found 13 papers, 8 papers with code

Demystifying Randomly Initialized Networks for Evaluating Generative Models

no code implementations19 Aug 2022 Junghyuk Lee, Jun-Hyuk Kim, Jong-Seok Lee

Our results indicate that the features from random networks can evaluate generative models well similarly to those from trained networks, and furthermore, the two types of features can be used together in a complementary way.

Joint Global and Local Hierarchical Priors for Learned Image Compression

1 code implementation CVPR 2022 Jun-Hyuk Kim, Byeongho Heo, Jong-Seok Lee

Recently, learned image compression methods have outperformed traditional hand-crafted ones including BPG.

Image Compression

Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack

no code implementations ICCV 2021 Jaehui Hwang, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee

In this paper, we study the structural vulnerability of deep learning-based action recognition models against the adversarial attack using the one frame attack that adds an inconspicuous perturbation to only a single frame of a given video clip.

Action Recognition Adversarial Attack

SRZoo: An integrated repository for super-resolution using deep learning

1 code implementation2 Jun 2020 Jun-Ho Choi, Jun-Hyuk Kim, Jong-Seok Lee

In addition, SRZoo provides platform-agnostic image reconstruction tools to obtain super-resolved images and evaluate the performance in place.

Image and Video Processing Multimedia

Deep unsupervised learning of turbulence for inflow generation at various Reynolds numbers

no code implementations28 Aug 2019 Jun-Hyuk Kim, Changhoon Lee

In the present work, we applied generative adversarial networks (GANs), a representative of unsupervised learning, to generate an inlet boundary condition of turbulent channel flow.

Fluid Dynamics Computational Physics

Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks

1 code implementation ICCV 2019 Jun-Ho Choi, huan zhang, Jun-Hyuk Kim, Cho-Jui Hsieh, Jong-Seok Lee

Single-image super-resolution aims to generate a high-resolution version of a low-resolution image, which serves as an essential component in many computer vision applications.

Image Super-Resolution

Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network

2 code implementations30 Nov 2018 Jun-Ho Choi, Jun-Hyuk Kim, Manri Cheon, Jong-Seok Lee

Recently, several deep learning-based image super-resolution methods have been developed by stacking massive numbers of layers.

Image Super-Resolution

MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution

3 code implementations29 Nov 2018 Jun-Hyuk Kim, Jun-Ho Choi, Manri Cheon, Jong-Seok Lee

Specifically, we propose a multi-path adaptive modulation block (MAMB), which is a lightweight yet effective residual block that adaptively modulates residual feature responses by fully exploiting their information via three paths.

Image Super-Resolution

Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality

1 code implementation13 Sep 2018 Jun-Ho Choi, Jun-Hyuk Kim, Manri Cheon, Jong-Seok Lee

Recently, it has been shown that in super-resolution, there exists a tradeoff relationship between the quantitative and perceptual quality of super-resolved images, which correspond to the similarity to the ground-truth images and the naturalness, respectively.

Image Super-Resolution

Generative adversarial network-based image super-resolution using perceptual content losses

1 code implementation13 Sep 2018 Manri Cheon, Jun-Hyuk Kim, Jun-Ho Choi, Jong-Seok Lee

In this paper, we propose a deep generative adversarial network for super-resolution considering the trade-off between perception and distortion.

Image Super-Resolution

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