Jaejun Yoo

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 10 papers; trusts 770 researchers and is trusted by 99 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 247 citations into it; #3,207 of 21,575 by trust here, #2,768 by citations
• Media Technology — 91 citations into it; #1,544 of 3,621 by trust here, #366 by citations
• Artificial Intelligence — 88 citations into it
• Generative Models — 56 citations into it; #360 of 1,956 by trust here, #529 by citations
• Radiology, Nuclear Medicine and Imaging — 34 citations into it; #2,315 of 3,265 by trust here, #1,923 by citations
• Model Architectures — 33 citations into it
• Computational Mechanics — 29 citations into it
• Evaluation & Benchmarks — 29 citations into it; #3,128 of 3,147 by trust here, #1,018 by citations

Whom Jaejun Yoo trusts overall

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

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

Who trusts Jaejun Yoo overall

1. Taesung Park
2. Mingxia Liu
3. Stephan R. Richter
4. Jun-Yan Zhu
5. Yuval Alaluf
6. Richard Zhang
7. Or Patashnik
8. Alexei A. Efros
9. Tero Karras
10. Jooyoung Choi

Papers from Jaejun Yoo's perspective

Ordered by how strongly the researchers Jaejun 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• StarGAN v2: Diverse Image Synthesis for Multiple Domains (2019) — 2,118 citations
• Reliable Fidelity and Diversity Metrics for Generative Models (2020) — 603 citations
• Photorealistic Style Transfer via Wavelet Transforms (2019) — 425 citations
• NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results (2017) — 1,901 citations
• Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification (2016) — 198 citations
• NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results (2020) — 190 citations
• Large-Scale Answerer in Questioner's Mind for Visual Dialog Question Generation (2019) — 18 citations
• Deep Learning with Domain Adaptation for Accelerated Projection Reconstruction MR (2017) — 245 citations
• Deep Convolutional Framelet Denosing for Low-Dose CT via Wavelet Residual Network (2017) — 306 citations
• Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks (2018) — 321 citations