Andrey Voynov

Computer Vision and Pattern Recognition · 2020–2022

A researcher in the Science corpus: 5 papers; trusts 758 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 107 citations into it; #4,284 of 21,575 by trust here, #7,249 by citations
• Generative Models — 76 citations into it; #398 of 1,956 by trust here, #846 by citations
• Artificial Intelligence — 33 citations into it; #5,911 of 35,345 by trust here, #10,667 by citations
• Representation & Self-Supervised Learning — 13 citations into it; #750 of 3,145 by trust here, #1,692 by citations
• Evaluation & Benchmarks — 10 citations into it; #2,342 of 3,147 by trust here, #3,112 by citations
• Media Technology — 10 citations into it
• Biophysics — 9 citations into it
• Computer Graphics and Computer-Aided Design — 8 citations into it

Whom Andrey Voynov trusts overall

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

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

Who trusts Andrey Voynov overall

1. Axel Sauer
2. Yuval Alaluf
3. Or Patashnik
4. Yujun Shen
5. Jiaolong Yang
6. Yotam Nitzan
7. Daniel Cohen-Or
8. Anton Cherepkov
9. Omer Tov
10. Daniel Roich

Papers from Andrey Voynov's perspective

Ordered by how strongly the researchers Andrey Voynov trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
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

• Unsupervised Discovery of Interpretable Directions in the GAN Latent Space (2020) — 463 citations
• Label-Efficient Semantic Segmentation with Diffusion Models (2021) — 723 citations
• Navigating the GAN Parameter Space for Semantic Image Editing (2020) — 79 citations
• When, Why, and Which Pretrained GANs Are Useful? (2022) — 25 citations
• On Self-Supervised Image Representations for GAN Evaluation (2021) — 44 citations