Carlos Domingo Castillo

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 8 papers; trusts 1,023 researchers and is trusted by 101 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 203 citations into it; #3,197 of 21,575 by trust here, #2,926 by citations
• Signal Processing — 66 citations into it; #947 of 7,572 by trust here, #664 by citations
• Artificial Intelligence — 63 citations into it; #11,501 of 35,345 by trust here, #10,065 by citations
• Representation & Self-Supervised Learning — 29 citations into it; #1,349 of 3,145 by trust here, #1,358 by citations
• Evaluation & Benchmarks — 28 citations into it
• Safety, Robustness & Interpretability — 22 citations into it
• Generative Models — 10 citations into it; #742 of 1,956 by trust here, #826 by citations
• Model Architectures — 10 citations into it

Whom Carlos Domingo Castillo trusts overall

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

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

Who trusts Carlos Domingo Castillo overall

1. Mohammad Rostami
2. Jiankang Deng
3. Feng Wang
4. S. Sankaranarayanan
5. Rajeev Ranjan
6. Yogesh Kumar Balaji
7. Ramalingam Chellappa
8. Jie Chang
9. Mei Wang
10. Ting-En Lin

Papers from Carlos Domingo Castillo's perspective

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

• L2-constrained Softmax Loss for Discriminative Face Verification (2017) — 482 citations
• Generate to Adapt: Aligning Domains Using Generative Adversarial Networks (2017) — 680 citations
• An All-In-One Convolutional Neural Network for Face Analysis (2016) — 450 citations
• Frontal to profile face verification in the wild (2016) — 842 citations
• SfSNet: Learning Shape, Reflectance and Illuminance of Faces 'in the Wild' (2017) — 343 citations
• UMDFaces: An annotated face dataset for training deep networks (2016) — 232 citations
• Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms (2018) — 361 citations
• Improving Network Robustness against Adversarial Attacks with Compact Convolution (2017) — 14 citations