Xu Zhang

Computer Vision and Pattern Recognition · with Chang · 2015–2019

A researcher in the Science corpus: 5 papers; trusts 598 researchers and is trusted by 267 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 117 citations into it; #2,444 of 21,575 by trust here, #4,690 by citations
• Artificial Intelligence — 55 citations into it; #11,506 of 35,345 by trust here, #8,439 by citations
• Representation & Self-Supervised Learning — 51 citations into it; #1,419 of 3,145 by trust here, #1,109 by citations
• Generative Models — 18 citations into it; #968 of 1,956 by trust here, #1,287 by citations
• Signal Processing — 14 citations into it
• Aerospace Engineering — 13 citations into it
• Model Architectures — 10 citations into it
• Evaluation & Benchmarks — 9 citations into it

Whom Xu Zhang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yann LeCun
5. David G. Lowe
6. Yoshua Bengio
7. Christian Szegedy
8. Karen Simonyan
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Xu Zhang overall

1. Mang Ye
2. Luisa Verdoliva
3. Novi Quadrianto
4. Felix X. Yu
5. Utkarsh Ojha
6. Kaiming He
7. Diego Gragnaniello
8. Xinlei Chen
9. Luca Guarnera
10. Ning Yu

Papers from Xu Zhang's perspective

Ordered by how strongly the researchers Xu Zhang 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Unsupervised Embedding Learning via Invariant and Spreading Instance Feature (2019) — 642 citations
• Detecting and Simulating Artifacts in GAN Fake Images (2019) — 652 citations
• Deep Transfer Network: Unsupervised Domain Adaptation (2015) — 143 citations
• Learning Spread-Out Local Feature Descriptors (2017) — 150 citations
• Heated-Up Softmax Embedding (2018) — 48 citations