Zhengli Zhao
Computer Vision and Pattern Recognition · with Singh · 2017–2020
A researcher in the Science corpus: 3 papers; trusts 401 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 89 citations into it; #8,850 of 21,575 by trust here, #5,287 by citations
- Artificial Intelligence — 68 citations into it; #17,777 of 35,345 by trust here, #8,365 by citations
- Generative Models — 54 citations into it; #1,289 of 1,956 by trust here, #1,181 by citations
- Representation & Self-Supervised Learning — 20 citations into it
- Optimization & Training — 19 citations into it
- Safety, Robustness & Interpretability — 17 citations into it; #1,492 of 2,251 by trust here, #889 by citations
- Natural Language Processing — 8 citations into it
- Signal Processing — 8 citations into it; #4,753 of 7,572 by trust here, #1,273 by citations
Whom Zhengli Zhao trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Kaiming He
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yann LeCun
- Yoshua Bengio
- Ian J. Goodfellow
- Ilya Sutskever
- Ross B. Girshick
- Individualized Treat
Who trusts Zhengli Zhao overall
- Jongheon Jeong
- Ceyuan Yang
- Jinwoo Shin
- James Thorne
- Akisato Kimura
- David Stutz
- Mingu Kang
- Zizhao Zhang
- W. Zhang
- Liwei Song
Papers from Zhengli Zhao's perspective
Ordered by how strongly the researchers Zhengli Zhao 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- Image Augmentations for GAN Training (2020) — 149 citations
- Generating Natural Adversarial Examples (2017) — 659 citations
- Improved Consistency Regularization for GANs (2020) — 167 citations