Byungkon Kang

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 542 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it; #6,099 of 21,575 by trust here, #8,952 by citations
• Media Technology — 34 citations into it; #929 of 3,621 by trust here, #1,079 by citations
• Artificial Intelligence — 20 citations into it
• Evaluation & Benchmarks — 9 citations into it
• Generative Models — 9 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 8 citations into it
• Efficiency & Compression — 7 citations into it

Whom Byungkon Kang trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Ilya Sutskever
6. Ross B. Girshick
7. Yoshua Bengio
8. Chao Dong
9. Jiwon Kim
10. Yann LeCun

Who trusts Byungkon Kang overall

1. Radu Timofte
2. Andreas Lugmayr
3. Saeed Anwar
4. Zhihao Wang
5. Jinjin Gu
6. Wanjie Sun
7. Xiangxiang Chu
8. Jie Liu
9. Zhisheng Lu
10. Namhyuk Ahn

Papers from Byungkon Kang's perspective

Ordered by how strongly the researchers Byungkon Kang 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Image Super-Resolution via Progressive Cascading Residual Network (2018) — 71 citations
• Fast, Accurate, and, Lightweight Super-Resolution with Cascading Residual Network (2018) — 1,424 citations
• Predicting Alzheimer’s disease progression using multi-modal deep learning approach (2019) — 385 citations