Ning Yu

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 950 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 296 citations into it; #9,945 of 21,575 by trust here, #6,671 by citations
• Generative Models — 182 citations into it; #1,267 of 1,956 by trust here, #1,415 by citations
• Artificial Intelligence — 148 citations into it; #30,599 of 35,345 by trust here, #19,873 by citations
• Signal Processing — 32 citations into it
• Optimization & Training — 28 citations into it
• Computer Graphics and Computer-Aided Design — 23 citations into it
• Media Technology — 22 citations into it
• Model Architectures — 21 citations into it

Whom Ning Yu trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Yoshua Bengio
6. Yann LeCun
7. Ilya Sutskever
8. Karen Simonyan
9. Ross B. Girshick
10. Christian Szegedy

Who trusts Ning Yu overall

1. Michael Albright
2. Ali Borji
3. Luisa Verdoliva
4. Francesco Marra
5. Diego Gragnaniello
6. Xu Zhang
7. Q. Liao
8. Pierre Fernandez
9. Weihao Xia
10. Yuyang Qian

Papers from Ning Yu's perspective

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

Their papers

• Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints (2018) — 553 citations
• Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data (2020) — 322 citations
• Inclusive GAN: Improving Data and Minority Coverage in Generative Models (2020) — 69 citations
• Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs (2020) — 31 citations
• Dual Contrastive Loss and Attention for GANs (2021) — 70 citations
• GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models (2019) — 514 citations
• Attributing Fake Images to GANs: Analyzing Fingerprints in Generated Images (2018) — 43 citations