Hankook Lee

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 812 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 130 citations into it
• Artificial Intelligence — 108 citations into it
• Representation & Self-Supervised Learning — 61 citations into it
• Optimization & Training — 26 citations into it
• Model Architectures — 24 citations into it
• Efficiency & Compression — 13 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Electrical and Electronic Engineering — 7 citations into it

Whom Hankook Lee trusts overall

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

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

Who trusts Hankook Lee overall

1. Jianping Gou
2. Jihoon Tack
3. Mingi Ji
4. Jaya Kalla
5. Baosheng Yu
6. Sangwoo Mo
7. Guile Wu
8. Khaled Alomar
9. Peng Chu
10. Fei Zhu

Papers from Hankook Lee's perspective

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

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

Their papers

• Patch-level Representation Learning for Self-supervised Vision Transformers (2022) — 89 citations
• Learning What and Where to Transfer (2019) — 165 citations
• Self-supervised Label Augmentation via Input Transformations (2019) — 223 citations
• Rethinking Data Augmentation: Self-Supervision and Self-Distillation (2019) — 66 citations