Jun-Hyuk Kim

Jun-Hyuk Kim

Yonsei University

H-index: 12

Asia-South Korea

About Jun-Hyuk Kim

Jun-Hyuk Kim, With an exceptional h-index of 12 and a recent h-index of 12 (since 2020), a distinguished researcher at Yonsei University, specializes in the field of Computer Vision, Machine Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Demystifying randomly initialized networks for evaluating generative models

Successive learned image compression: Comprehensive analysis of instability

Joint global and local hierarchical priors for learned image compression

Deep image destruction: Vulnerability of deep image-to-image models against adversarial attacks

Volatile-nonvolatile memory network for progressive image super-resolution

Just one moment: Structural vulnerability of deep action recognition against one frame attack

Instability of successive deep image compression

LarvaNet: Hierarchical super-resolution via multi-exit architecture

Jun-Hyuk Kim Information

University

Position

___

Citations(all)

966

Citations(since 2020)

930

Cited By

322

hIndex(all)

12

hIndex(since 2020)

12

i10Index(all)

12

i10Index(since 2020)

12

Email

University Profile Page

Google Scholar

Jun-Hyuk Kim Skills & Research Interests

Computer Vision

Machine Learning

Top articles of Jun-Hyuk Kim

Demystifying randomly initialized networks for evaluating generative models

Proceedings of the AAAI Conference on Artificial Intelligence

2023/6/26

Successive learned image compression: Comprehensive analysis of instability

Neurocomputing

2022/9/28

Joint global and local hierarchical priors for learned image compression

2022/6

Deep image destruction: Vulnerability of deep image-to-image models against adversarial attacks

arXiv preprint arXiv:2104.15022

2021/4/30

Volatile-nonvolatile memory network for progressive image super-resolution

IEEE Access

2021/3/4

Just one moment: Structural vulnerability of deep action recognition against one frame attack

2021

Instability of successive deep image compression

2020/10/12

LarvaNet: Hierarchical super-resolution via multi-exit architecture

2020

Deep learning-based image super-resolution considering quantitative and perceptual quality

arXiv preprint arXiv:1809.04789

2018/9/13

Efficient deep learning-based lossy image compression via asymmetric autoencoder and pruning

2020/5/4

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

2020/5/4

Multi-scale adaptive residual network using total variation for real image super-resolution

2020/11/1

Efficient bokeh effect rendering using generative adversarial network

2020/11/1

Adversarially robust deep image super-resolution using entropy regularization

2020

MAMNet: Multi-path adaptive modulation network for image super-resolution

Neurocomputing

2020/8/18

See List of Professors in Jun-Hyuk Kim University(Yonsei University)

Co-Authors

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