Donggeun Yoo

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 8 papers; trusts 1,309 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 228 citations into it; #8,891 of 21,575 by trust here, #7,176 by citations
• Artificial Intelligence — 149 citations into it; #15,436 of 35,345 by trust here, #21,878 by citations
• Representation & Self-Supervised Learning — 69 citations into it; #1,866 of 3,145 by trust here, #1,460 by citations
• Generative Models — 24 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Media Technology — 16 citations into it
• Model Architectures — 16 citations into it
• Optimization & Training — 14 citations into it

Whom Donggeun Yoo trusts overall

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

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

Who trusts Donggeun Yoo overall

1. Liang Zheng
2. Siwei Yu
3. Ali Ayub
4. Zhaowei Cai
5. Devendra Singh Chaplot
6. Garrett Wilson
7. Yi Yang
8. Prateek Munjal
9. Yanming Guo
10. Pengzhen Ren

Papers from Donggeun Yoo's perspective

Ordered by how strongly the researchers Donggeun Yoo 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• AttentionNet: Aggregating Weak Directions for Accurate Object Detection (2015) — 167 citations
• Learning Image Representations by Completing Damaged Jigsaw Puzzles (2018) — 170 citations
• Multi-scale pyramid pooling for deep convolutional representation (2015) — 102 citations
• Learning Loss for Active Learning (2019) — 826 citations
• Reducing Domain Gap by Reducing Style Bias (2021) — 471 citations
• Reducing Domain Gap via Style-Agnostic Networks (2019) — 66 citations
• Pixel-Level Domain Transfer (2016) — 331 citations
• Learning a Deep Convolutional Network for Light-Field Image Super-Resolution (2015) — 378 citations