Seunghyun Lee

Computer Vision and Pattern Recognition · with Song · 2018–2022

A researcher in the Science corpus: 4 papers; trusts 746 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it; #6,979 of 21,575 by trust here, #11,498 by citations
• Artificial Intelligence — 71 citations into it; #10,101 of 35,345 by trust here, #16,166 by citations
• Model Architectures — 36 citations into it
• Optimization & Training — 16 citations into it
• Efficiency & Compression — 14 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Media Technology — 7 citations into it
• Natural Language Processing — 7 citations into it

Whom Seunghyun Lee trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Karen Simonyan
5. Yoshua Bengio
6. Ross B. Girshick
7. Andrew Zisserman
8. Yann LeCun
9. Ilya Sutskever
10. Christian Szegedy

Who trusts Seunghyun Lee overall

1. Linfeng Zhang
2. Jianping Gou
3. Lin Wang
4. Mingkuan Yuan
5. Anbang Yao
6. Mengya Gao
7. Baosheng Yu
8. Xiaoyu Tao
9. Cheng Chen
10. Qiankun Gao

Papers from Seunghyun Lee's perspective

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

Their papers

• Self-supervised Knowledge Distillation Using Singular Value Decomposition (2018) — 153 citations
• Graph-based Knowledge Distillation by Multi-head Attention Network (2019) — 89 citations
• CFA: Coupled-Hypersphere-Based Feature Adaptation for Target-Oriented Anomaly Localization (2022) — 336 citations
• Vision Transformer for Small-Size Datasets (2021) — 307 citations