Lei Zhu
Computer Vision and Pattern Recognition · 2017–2021
A researcher in the Science corpus: 10 papers; trusts 777 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 187 citations into it; #11,857 of 21,575 by trust here, #7,938 by citations
- Artificial Intelligence — 155 citations into it; #22,368 of 35,345 by trust here, #13,962 by citations
- Information Systems — 68 citations into it; #4,831 of 8,771 by trust here, #3,640 by citations
- Representation & Self-Supervised Learning — 51 citations into it
- Recommender Systems — 28 citations into it
- Model Architectures — 18 citations into it
- Optimization & Training — 17 citations into it
- Media Technology — 16 citations into it; #2,042 of 3,621 by trust here, #1,017 by citations
Whom Lei Zhu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Diederik P. Kingma
- Yoshua Bengio
- Karen Simonyan
- Ross B. Girshick
- Yann LeCun
- Ilya Sutskever
- Andrew Zisserman
Who trusts Lei Zhu overall
- He Zhang
- Xueyang Fu
- Vishal M. Patel
- Xiaowei Hu
- Jie Xiao
- Wei Wei
- Dongwei Ren
- Tianyu Wang
- Dongdong Chen
- Deyu Meng
Papers from Lei Zhu's perspective
Ordered by how strongly the researchers Lei Zhu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Leveraging the Invariant Side of Generative Zero-Shot Learning (2019) — 338 citations
- R³Net: Recurrent Residual Refinement Network for Saliency Detection (2018) — 518 citations
- Joint Bi-layer Optimization for Single-Image Rain Streak Removal (2017) — 333 citations
- Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation (2021) — 208 citations
- Interest-aware Message-Passing GCN for Recommendation (2021) — 332 citations
- Maximum Density Divergence for Domain Adaptation (2020) — 340 citations
- Depth-Attentional Features for Single-Image Rain Removal (2019) — 400 citations
- CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading (2019) — 367 citations
- Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews (2018) — 322 citations
- MMALFM (2018) — 170 citations