Ismail Ben Ayed

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 7 papers; trusts 379 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 152 citations into it; #20,517 of 21,575 by trust here, #8,722 by citations
• Artificial Intelligence — 106 citations into it
• Model Architectures — 22 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Radiology, Nuclear Medicine and Imaging — 18 citations into it; #3,087 of 3,265 by trust here, #2,321 by citations
• Safety, Robustness & Interpretability — 17 citations into it
• Computational Mechanics — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Ismail Ben Ayed trusts overall

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

1. Kaiming He
2. Yuri Boykov
3. Radford M. Neal
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Yann LeCun
7. Yoshua Bengio
8. Christian Szegedy
9. Ross B. Girshick
10. David G. Lowe

Who trusts Ismail Ben Ayed overall

1. Anna Khoreva

Papers from Ismail Ben Ayed's perspective

Ordered by how strongly the researchers Ismail Ben Ayed 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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

• Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need? (2020) — 240 citations
• On Regularized Losses for Weakly-supervised CNN Segmentation (2018) — 327 citations
• Secrets of GrabCut and Kernel K-Means (2015) — 30 citations
• Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses (2018) — 327 citations
• HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation (2018) — 515 citations
• Boundary loss for highly unbalanced segmentation (2018) — 736 citations
• 3D fully convolutional networks for subcortical segmentation in MRI: A large‐scale study (2016) — 388 citations