Ngoc-Trung Tran

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 6 papers; trusts 684 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 184 citations into it; #7,458 of 21,575 by trust here, #8,409 by citations
• Generative Models — 176 citations into it; #1,588 of 1,956 by trust here, #1,485 by citations
• Artificial Intelligence — 114 citations into it; #15,989 of 35,345 by trust here, #14,181 by citations
• Representation & Self-Supervised Learning — 38 citations into it
• Statistical and Nonlinear Physics — 37 citations into it
• Optimization & Training — 33 citations into it
• Probabilistic & Bayesian Methods — 15 citations into it
• Signal Processing — 12 citations into it; #4,574 of 7,572 by trust here, #6,591 by citations

Whom Ngoc-Trung Tran trusts overall

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

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

Who trusts Ngoc-Trung Tran overall

1. Viet-Hung Tran
2. Ngoc-Bao Nguyen
3. Jongheon Jeong
4. Ngai-Man Cheung
5. Kwot Sin Lee
6. Sam Bond-Taylor
7. Jinwoo Shin
8. Alexia Jolicoeur-Martineau
9. Ning Yu
10. Axel Sauer

Papers from Ngoc-Trung Tran's perspective

Ordered by how strongly the researchers Ngoc-Trung Tran 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• On Data Augmentation for GAN Training (2020) — 344 citations
• Dist-GAN: An Improved GAN Using Distance Constraints (2018) — 97 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