Kyungnam Kim

Computer Vision and Pattern Recognition · 2004–2019

A researcher in the Science corpus: 8 papers; trusts 457 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 118 citations into it; #10,298 of 21,575 by trust here, #6,031 by citations
• Artificial Intelligence — 113 citations into it; #16,471 of 35,345 by trust here, #8,810 by citations
• Representation & Self-Supervised Learning — 63 citations into it
• Computational Mechanics — 26 citations into it
• Generative Models — 18 citations into it
• Optimization & Training — 15 citations into it
• Cancer Research — 13 citations into it; #922 of 1,780 by trust here, #860 by citations
• Radiology, Nuclear Medicine and Imaging — 11 citations into it

Whom Kyungnam Kim trusts overall

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

1. Yann LeCun
2. Yoshua Bengio
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Kaiming He
7. Ilya Sutskever
8. Karen Simonyan
9. Jimmy Ba
10. Ross B. Girshick

Who trusts Kyungnam Kim overall

1. Thierry Bouwmans
2. Olivier Barnich
3. Sebastian Brutzer
4. Mohammad Rostami
5. Marc Van Droogenbroeck
6. Marko Heikkilä
7. James W. Davis
8. Mohammadreza Babaee
9. Pierre-Luc St-Charles
10. Benjamin Höferlin

Papers from Kyungnam Kim's perspective

Ordered by how strongly the researchers Kyungnam Kim 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Image to Image Translation for Domain Adaptation (2017) — 561 citations
• Real-time foreground-background segmentation using codebook model (2005) — 1,613 citations
• SAR Image Classification Using Few-Shot Cross-Domain Transfer Learning (2019) — 61 citations
• Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment (2019) — 24 citations
• Deep Transfer Learning for Few-Shot SAR Image Classification (2019) — 199 citations
• Joint Dictionaries for Zero-Shot Learning (2017) — 26 citations
• Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition (2017) — 39 citations
• Background modeling and subtraction by codebook construction (2004) — 437 citations