Alvaro Fuentes

Computer Vision and Pattern Recognition · 2017–2022

A researcher in the Science corpus: 2 papers; trusts 335 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 87 citations into it
• Artificial Intelligence — 57 citations into it
• Representation & Self-Supervised Learning — 18 citations into it
• Model Architectures — 13 citations into it
• Optimization & Training — 11 citations into it
• Plant Science — 7 citations into it; #5,126 of 8,232 by trust here, #3,226 by citations
• Media Technology — 6 citations into it

Whom Alvaro Fuentes trusts overall

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

1. Kaiming He
2. David G. Lowe
3. Geoffrey E. Hinton
4. Christian Szegedy
5. Ross B. Girshick
6. Alex Krizhevsky
7. Shaoqing Ren
8. Robert E. Schapire
9. Navneet Dalal
10. Yoshua Bengio

Who trusts Alvaro Fuentes overall

1. Yu Jiang
2. Muhammad Hammad Saleem
3. Lili Li
4. José G. M. Esgario
5. Anshul Bhatia
6. Marko Arsenovic
7. J. G. Arnal Barbedo
8. Vijaypal Singh Dhaka
9. Chowdhury Rafeed Rahman
10. Changying Li

Papers from Alvaro Fuentes's perspective

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

Their papers

• A Comprehensive Survey of Image Augmentation Techniques for Deep Learning (2022) — 642 citations
• A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition (2017) — 1,358 citations