Lucy Chai

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 5 papers; trusts 825 researchers and is trusted by 76 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 218 citations into it; #3,473 of 21,575 by trust here, #5,422 by citations
• Generative Models — 107 citations into it; #383 of 1,956 by trust here, #711 by citations
• Artificial Intelligence — 66 citations into it; #23,486 of 35,345 by trust here, #18,186 by citations
• Safety, Robustness & Interpretability — 26 citations into it; #1,960 of 2,251 by trust here, #1,780 by citations
• Evaluation & Benchmarks — 22 citations into it
• Media Technology — 20 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Computer Graphics and Computer-Aided Design — 19 citations into it

Whom Lucy Chai trusts overall

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

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

Who trusts Lucy Chai overall

1. Ali Borji
2. Yotam Nitzan
3. Axel Sauer
4. Jun-Yan Zhu
5. Weihao Xia
6. Eli Shechtman
7. Richard Zhang
8. Zongze Wu
9. Utkarsh Ojha
10. Diego Gragnaniello

Papers from Lucy Chai's perspective

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

Their papers

• On the "steerability" of generative adversarial networks (2019) — 426 citations
• Ensembling with Deep Generative Views (2021) — 74 citations
• Using latent space regression to analyze and leverage compositionality in GANs (2021) — 82 citations
• What makes fake images detectable? Understanding properties that generalize (2020) — 454 citations
• DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data (2023) — 356 citations