Linfeng Zhang
Computer Vision and Pattern Recognition · with Bao · 2019–2021
A researcher in the Science corpus: 3 papers; trusts 823 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 146 citations into it
- Artificial Intelligence — 125 citations into it
- Efficiency & Compression — 73 citations into it
- Model Architectures — 43 citations into it
- Optimization & Training — 15 citations into it
- Representation & Self-Supervised Learning — 11 citations into it
- Media Technology — 6 citations into it
- Natural Language Processing — 6 citations into it
Whom Linfeng Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Yoshua Bengio
- Ross B. Girshick
- Yann LeCun
- Diederik P. Kingma
- Karen Simonyan
- Ilya Sutskever
- Sergey Zagoruyko
Who trusts Linfeng Zhang overall
- Jianping Gou
- Hankook Lee
- Chuanguang Yang
- Baosheng Yu
- Liyuan Sun
- Guohao Li
- Ji Xin
- Hongwei Yan
- Sukmin Yun
- Yunteng Luan
Papers from Linfeng Zhang's perspective
Ordered by how strongly the researchers Linfeng Zhang 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation (2019) — 1,120 citations
- Self-Distillation: Towards Efficient and Compact Neural Networks (2021) — 319 citations
- Task-Oriented Feature Distillation (2020) — 73 citations