Terrance Devries

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 899 researchers and is trusted by 2,765 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 168 citations into it; #744 of 21,575 by trust here, #1,749 by citations
• Artificial Intelligence — 104 citations into it; #467 of 35,345 by trust here, #1,924 by citations
• Generative Models — 66 citations into it
• Evaluation & Benchmarks — 37 citations into it
• Representation & Self-Supervised Learning — 28 citations into it; #801 of 3,145 by trust here, #1,671 by citations
• Safety, Robustness & Interpretability — 27 citations into it; #469 of 2,251 by trust here, #729 by citations
• Model Architectures — 20 citations into it
• Optimization & Training — 20 citations into it; #103 of 2,601 by trust here, #270 by citations

Whom Terrance Devries trusts overall

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

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

Who trusts Terrance Devries overall

1. Ekin Dogus Cubuk
2. Barret Zoph
3. Martin Wistuba
4. Quoc V. Le
5. Kihyuk Sohn
6. Dan Hendrycks
7. Kaiyang Zhou
8. Zizhao Zhang
9. Bernt Schiele
10. Alan L. Yuille

Papers from Terrance Devries's perspective

Ordered by how strongly the researchers Terrance Devries 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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

• Improved Regularization of Convolutional Neural Networks with Cutout (2017) — 4,336 citations
• Does Object Recognition Work for Everyone? (2019) — 293 citations
• Dataset Augmentation in Feature Space (2017) — 460 citations
• Instance Selection for GANs (2020) — 41 citations
• Learning Confidence for Out-of-Distribution Detection in Neural Networks (2018) — 664 citations
• ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis (2020) — 196 citations
• On the Evaluation of Conditional GANs (2019) — 51 citations