Yunjey Choi

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 360 researchers and is trusted by 260 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 81 citations into it; #2,440 of 21,575 by trust here, #2,169 by citations
• Generative Models — 60 citations into it; #304 of 1,956 by trust here, #252 by citations
• Artificial Intelligence — 26 citations into it
• Evaluation & Benchmarks — 13 citations into it; #3,132 of 3,147 by trust here, #2,627 by citations
• Optimization & Training — 12 citations into it
• Model Architectures — 5 citations into it

Whom Yunjey Choi 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. Christian Szegedy
6. Yoshua Bengio
7. Yann LeCun
8. Jimmy Ba
9. Ilya Sutskever
10. Sergey Ioffe

Who trusts Yunjey Choi overall

1. Robin Rombach
2. Yuval Alaluf
3. Or Patashnik
4. Yotam Nitzan
5. Tero Karras
6. Yang Zhao
7. Richard Zhang
8. Taesung Park
9. Zongze Wu
10. Patrick Esser

Papers from Yunjey Choi's perspective

Ordered by how strongly the researchers Yunjey Choi 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation (2017) — 3,914 citations
• StarGAN v2: Diverse Image Synthesis for Multiple Domains (2019) — 2,118 citations
• Reliable Fidelity and Diversity Metrics for Generative Models (2020) — 603 citations