J
Jung Kwon Lee
Researcher at Seoul National University
Publications - 11
Citations - 14855
Jung Kwon Lee is an academic researcher from Seoul National University. The author has contributed to research in topics: Gradient descent & Generative model. The author has an hindex of 9, co-authored 11 publications receiving 11307 citations.
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Proceedings ArticleDOI
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
TL;DR: In this article, a very deep convolutional network inspired by VGG-net was used for image superresolution, which achieved state-of-the-art performance in accuracy.
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Accurate Image Super-Resolution Using Very Deep Convolutional Networks
TL;DR: This work presents a highly accurate single-image superresolution (SR) method using a very deep convolutional network inspired by VGG-net used for ImageNet classification and uses extremely high learning rates enabled by adjustable gradient clipping.
Proceedings ArticleDOI
Deeply-Recursive Convolutional Network for Image Super-Resolution
TL;DR: In this paper, a deeply-recursive convolutional network (DRCN) was proposed for image super-resolution using a very deep recursive layer (up to 16 recursions).
Posted Content
Deeply-Recursive Convolutional Network for Image Super-Resolution
TL;DR: This work proposes an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN) with two extensions: recursive-supervision and skip-connection, which outperforms previous methods by a large margin.
Proceedings Article
Learning to discover cross-domain relations with generative adversarial networks
TL;DR: DiscoGAN as mentioned in this paper proposes a method based on GANs that learns to discover relations between different domains (discoGAN) using the discovered relations, which successfully transfers style from one domain to another while preserving key attributes of orientation and face identity.