Wonho Choo

Computer Vision and Pattern Recognition · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 400 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 46 citations into it; #21,360 of 21,575 by trust here, #15,529 by citations
• Artificial Intelligence — 34 citations into it; #34,995 of 35,345 by trust here, #23,731 by citations
• Optimization & Training — 14 citations into it
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Safety, Robustness & Interpretability — 6 citations into it

Whom Wonho Choo trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Karen Simonyan
4. Radford M. Neal
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. X. Zhang
8. Ross B. Girshick
9. Andrew Zisserman
10. Ilya Sutskever

Who trusts Wonho Choo overall

1. Jang-Hyun Kim
2. Jihwan Bang
3. Linjun Zhang
4. Jie Qin

Papers from Wonho Choo's perspective

Ordered by how strongly the researchers Wonho Choo 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity (2021) — 215 citations
• Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup (2020) — 471 citations