Jung Kwon Lee

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 5 papers; trusts 574 researchers and is trusted by 2,190 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 91 citations into it; #851 of 21,575 by trust here, #553 by citations
• Artificial Intelligence — 55 citations into it; #1,556 of 35,345 by trust here, #1,590 by citations
• Generative Models — 27 citations into it; #912 of 1,956 by trust here, #411 by citations
• Media Technology — 23 citations into it; #33 of 3,621 by trust here, #69 by citations
• Model Architectures — 22 citations into it
• Optimization & Training — 16 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Computational Mechanics — 8 citations into it

Whom Jung Kwon Lee trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Yann LeCun
3. Yoshua Bengio
4. Alex Krizhevsky
5. Kaiming He
6. Diederik P. Kingma
7. Radford M. Neal
8. Karen Simonyan
9. Ilya Sutskever
10. L. Eon Bottou

Who trusts Jung Kwon Lee overall

1. Kai Zhang
2. Yulun Zhang
3. Zixuan Ke
4. Saeed Anwar
5. Fei Ye
6. Mohammad Rostami
7. Wangmeng Zuo
8. Timothée Lesort
9. Radu Timofte
10. Namhyuk Ahn

Papers from Jung Kwon Lee's perspective

Ordered by how strongly the researchers Jung Kwon Lee trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Continual Learning with Deep Generative Replay (2017) — 2,523 citations
• Learning to Discover Cross-Domain Relations with Generative Adversarial Networks (2017) — 2,101 citations
• Accurate Image Super-Resolution Using Very Deep Convolutional Networks (2015) — 7,077 citations
• Deeply-Recursive Convolutional Network for Image Super-Resolution (2015) — 2,772 citations
• Auto-Meta: Automated Gradient Based Meta Learner Search (2018) — 44 citations