Amir Atapour-Abarghouei

Computer Vision and Pattern Recognition · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 842 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 95 citations into it; #6,493 of 21,575 by trust here, #8,205 by citations
• Artificial Intelligence — 56 citations into it; #10,368 of 35,345 by trust here, #13,962 by citations
• Generative Models — 53 citations into it
• Computer Networks and Communications — 16 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Media Technology — 11 citations into it; #2,431 of 3,621 by trust here, #2,389 by citations
• Optimization & Training — 11 citations into it
• Model Architectures — 8 citations into it

Whom Amir Atapour-Abarghouei trusts overall

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

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

Who trusts Amir Atapour-Abarghouei overall

1. Garrett Wilson
2. Vitjan Zavrtanik
3. Yongcheng Jing
4. Diane Joyce Cook
5. Hanqiu Deng
6. Erik A. Daxberger
7. Shu Kong
8. Trong-Nguyen Nguyen
9. Matej Kristan
10. Kilian Batzner

Papers from Amir Atapour-Abarghouei's perspective

Ordered by how strongly the researchers Amir Atapour-Abarghouei 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
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

• GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training (2018) — 1,738 citations
• Real-Time Monocular Depth Estimation Using Synthetic Data with Domain Adaptation via Image Style Transfer (2018) — 294 citations
• Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection (2019) — 461 citations