Moonsu Cha

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 205 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 54 citations into it; #5,873 of 21,575 by trust here, #3,598 by citations
• Artificial Intelligence — 35 citations into it
• Generative Models — 28 citations into it; #706 of 1,956 by trust here, #400 by citations
• Representation & Self-Supervised Learning — 14 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 9 citations into it

Whom Moonsu Cha trusts overall

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

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

Who trusts Moonsu Cha overall

1. Sagie Benaim
2. Yongjun Hong
3. Xiang Wang
4. Hsin-Ying Lee
5. Xun Huang
6. Xingchao Peng
7. Garrett Wilson
8. Lior Wolf
9. Mei Wang
10. Ming-Yu Liu

Papers from Moonsu Cha's perspective

Ordered by how strongly the researchers Moonsu Cha 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning to Discover Cross-Domain Relations with Generative Adversarial Networks (2017) — 2,101 citations
• Auto-Meta: Automated Gradient Based Meta Learner Search (2018) — 44 citations
• Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks (2017) — 42 citations