Kaiyue Pang

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 231 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 94 citations into it; #6,445 of 21,575 by trust here, #7,938 by citations
• Generative Models — 40 citations into it
• Artificial Intelligence — 39 citations into it; #23,408 of 35,345 by trust here, #23,731 by citations
• Representation & Self-Supervised Learning — 13 citations into it
• Computer Graphics and Computer-Aided Design — 8 citations into it
• Media Technology — 8 citations into it
• Optimization & Training — 8 citations into it
• Model Architectures — 6 citations into it

Whom Kaiyue Pang trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Yoshua Bengio
6. Ronald J. Williams
7. Kaiming He
8. Alex Graves
9. Ilya Sutskever
10. Yann LeCun

Who trusts Kaiyue Pang overall

1. Ayan Kumar Bhunia
2. Aneeshan Sain
3. Yi-Zhe Song
4. Pinaki Nath Chowdhury
5. Tao A. Xiang
6. Yongxin Yang
7. Xiu-Shen Wei
8. Timothy M. Hospedales
9. Jifei Song
10. Peng Xu

Papers from Kaiyue Pang's perspective

Ordered by how strongly the researchers Kaiyue Pang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Cross-domain Generative Learning for Fine-Grained Sketch-Based Image Retrieval (2017) — 76 citations
• Learning to Sketch with Shortcut Cycle Consistency (2018) — 137 citations
• Generalising Fine-Grained Sketch-Based Image Retrieval (2019) — 97 citations
• SketchMate: Deep Hashing for Million-Scale Human Sketch Retrieval (2018) — 125 citations