Ngai-Man Cheung

Computer Vision and Pattern Recognition · with Tran · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 780 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 198 citations into it; #6,490 of 21,575 by trust here, #7,584 by citations
• Generative Models — 176 citations into it; #1,587 of 1,956 by trust here, #1,485 by citations
• Artificial Intelligence — 120 citations into it; #15,988 of 35,345 by trust here, #14,181 by citations
• Representation & Self-Supervised Learning — 41 citations into it
• Statistical and Nonlinear Physics — 38 citations into it
• Optimization & Training — 34 citations into it; #1,800 of 2,601 by trust here, #2,215 by citations
• Probabilistic & Bayesian Methods — 15 citations into it
• Media Technology — 14 citations into it; #2,018 of 3,621 by trust here, #2,706 by citations

Whom Ngai-Man Cheung trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Yann LeCun
6. Kaiming He
7. Ian J. Goodfellow
8. Ilya Sutskever
9. Alec Radford
10. Jeff Donahue

Who trusts Ngai-Man Cheung overall

1. Ngoc-Trung Tran
2. Viet-Hung Tran
3. Samuel F. Dodge
4. Jongheon Jeong
5. Ngoc-Bao Nguyen
6. Lina Karam
7. Jinwoo Shin

Papers from Ngai-Man Cheung's perspective

Ordered by how strongly the researchers Ngai-Man Cheung 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• On Data Augmentation for GAN Training (2020) — 344 citations
• Dist-GAN: An Improved GAN Using Distance Constraints (2018) — 97 citations
• On classification of distorted images with deep convolutional neural networks (2017) — 121 citations
• InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning (2020) — 85 citations
• An Improved Self-supervised GAN via Adversarial Training (2019) — 14 citations
• Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game (2019) — 68 citations
• Improving GAN with neighbors embedding and gradient matching (2018) — 16 citations