Jie Zhang

Computer Vision and Pattern Recognition · with Zhang · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 189 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it; #20,127 of 21,575 by trust here, #6,873 by citations
• Artificial Intelligence — 60 citations into it; #32,875 of 35,345 by trust here, #9,760 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Model Architectures — 15 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Efficiency & Compression — 5 citations into it
• Signal Processing — 5 citations into it

Whom Jie 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. Karen Simonyan
6. Ross B. Girshick
7. Yoshua Bengio
8. Ilya Sutskever
9. Jia Deng
10. Andrew Zisserman

Who trusts Jie Zhang overall

1. Rahaf Aljundi
2. Da-Wei Zhou
3. B. Ermiş
4. Marc Masana
5. Tianlong Chen
6. Matthias De Lange
7. Giovanni Zappella
8. Umberto Michieli
9. Liyuan Wang
10. Alexis Lechat

Papers from Jie Zhang's perspective

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

Their papers

• Class-incremental Learning via Deep Model Consolidation (2019) — 390 citations
• Regularize, Expand and Compress: NonExpansive Continual Learning (2020) — 27 citations