Samet Akcay

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 617 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 77 citations into it; #6,882 of 21,575 by trust here, #8,831 by citations
• Artificial Intelligence — 57 citations into it; #10,367 of 35,345 by trust here, #13,962 by citations
• Generative Models — 29 citations into it
• Computer Networks and Communications — 16 citations into it
• Optimization & Training — 11 citations into it
• Model Architectures — 10 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Biophysics — 6 citations into it; #208 of 1,196 by trust here, #510 by citations

Whom Samet Akcay trusts overall

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

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

Who trusts Samet Akcay overall

1. Vitjan Zavrtanik
2. Hanqiu Deng
3. Erik A. Daxberger
4. Shu Kong
5. Trong-Nguyen Nguyen
6. Matej Kristan
7. Kilian Batzner
8. Amir Atapour-Abarghouei
9. Chun-Liang Li
10. Guodong Wang

Papers from Samet Akcay's perspective

Ordered by how strongly the researchers Samet Akcay 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training (2018) — 1,738 citations
• Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery (2018) — 329 citations
• An evaluation of region based object detection strategies within X-ray baggage security imagery (2017) — 84 citations
• Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection (2019) — 461 citations