Youngjung Uh

Computer Vision and Pattern Recognition · 2019–2022

A researcher in the Science corpus: 4 papers; trusts 523 researchers and is trusted by 79 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 106 citations into it; #3,470 of 21,575 by trust here, #4,865 by citations
• Generative Models — 64 citations into it; #361 of 1,956 by trust here, #556 by citations
• Artificial Intelligence — 33 citations into it
• Evaluation & Benchmarks — 18 citations into it; #3,131 of 3,147 by trust here, #2,627 by citations
• Optimization & Training — 12 citations into it
• Biophysics — 10 citations into it
• Media Technology — 10 citations into it
• Model Architectures — 10 citations into it

Whom Youngjung Uh trusts overall

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

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

Who trusts Youngjung Uh overall

1. Taesung Park
2. Yuval Alaluf
3. Or Patashnik
4. Tero Karras
5. Yotam Nitzan
6. Richard Zhang
7. Jooyoung Choi
8. Axel Sauer
9. Eli Shechtman
10. Zongze Wu

Papers from Youngjung Uh's perspective

Ordered by how strongly the researchers Youngjung Uh 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

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
• Diffusion Models already have a Semantic Latent Space (2022) — 387 citations