Hongguang Zhang
Computer Vision and Pattern Recognition · 2019
A researcher in the Science corpus: 2 papers; trusts 281 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 47 citations into it; #14,282 of 21,575 by trust here, #10,677 by citations
- Artificial Intelligence — 21 citations into it; #28,115 of 35,345 by trust here, #25,809 by citations
- Representation & Self-Supervised Learning — 16 citations into it; #2,624 of 3,145 by trust here, #2,691 by citations
- Media Technology — 14 citations into it; #2,193 of 3,621 by trust here, #1,135 by citations
Whom Hongguang Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Ross B. Girshick
- Karen Simonyan
- Ilya Sutskever
- Yoshua Bengio
- Andrew Zisserman
- Yann LeCun
Who trusts Hongguang Zhang overall
- Ammar Mohammed
- Yuqing Hu
- Chong Mou
- Zhongqi Yue
- Jaesung Rim
- Maitreya Suin
- Kai Zhang
- Yisheng Song
- Zhendong Wang
- M. A. Ganaie
Papers from Hongguang Zhang's perspective
Ordered by how strongly the researchers Hongguang Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- 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
- Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring (2019) — 649 citations
- Few-Shot Learning via Saliency-Guided Hallucination of Samples (2019) — 216 citations