Kwonjoon Lee

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 738 researchers and is trusted by 82 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 152 citations into it; #3,417 of 21,575 by trust here, #4,584 by citations
• Artificial Intelligence — 82 citations into it; #7,099 of 35,345 by trust here, #7,346 by citations
• Generative Models — 67 citations into it; #1,754 of 1,956 by trust here, #1,635 by citations
• Representation & Self-Supervised Learning — 28 citations into it
• Optimization & Training — 20 citations into it; #975 of 2,601 by trust here, #759 by citations
• Model Architectures — 19 citations into it
• Probabilistic & Bayesian Methods — 17 citations into it; #995 of 1,525 by trust here, #1,136 by citations
• Evaluation & Benchmarks — 9 citations into it

Whom Kwonjoon Lee trusts overall

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

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

Who trusts Kwonjoon Lee overall

1. Han-Jia Ye
2. Erik Nijkamp
3. De-chuan Zhan
4. Hankook Lee
5. Jianwen Xie
6. Song-Chun Zhu
7. Hexiang Hu
8. Micah Goldblum
9. Ruiqi Gao
10. Ying Nian Wu

Papers from Kwonjoon Lee's perspective

Ordered by how strongly the researchers Kwonjoon 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. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Meta-Learning With Differentiable Convex Optimization (2019) — 1,446 citations
• Learning Instance Occlusion for Panoptic Segmentation (2019) — 78 citations
• Wasserstein Introspective Neural Networks (2017) — 61 citations
• Dual Contradistinctive Generative Autoencoder (2020) — 94 citations