Assaf Shocher

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 581 researchers and is trusted by 63 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 105 citations into it; #3,748 of 21,575 by trust here, #6,371 by citations
• Media Technology — 44 citations into it; #319 of 3,621 by trust here, #603 by citations
• Generative Models — 33 citations into it; #329 of 1,956 by trust here, #717 by citations
• Artificial Intelligence — 20 citations into it
• Optimization & Training — 10 citations into it
• Evaluation & Benchmarks — 8 citations into it
• Computational Mechanics — 7 citations into it
• Model Architectures — 5 citations into it

Whom Assaf Shocher trusts overall

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

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

Who trusts Assaf Shocher overall

1. Kai Zhang
2. Radu Timofte
3. Roey Mechrez
4. Andreas Lugmayr
5. Yochai Blau
6. Luc Van Gool
7. Jingyun Liang
8. Zhengxiong Luo
9. Wanjie Sun
10. Dongwei Ren

Papers from Assaf Shocher's perspective

Ordered by how strongly the researchers Assaf Shocher 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Zero-Shot Super-Resolution Using Deep Internal Learning (2017) — 980 citations
• Semantic Pyramid for Image Generation (2020) — 58 citations
• Blind Super-Resolution Kernel Estimation using an Internal-GAN (2019) — 533 citations
• “Double-DIP”: Unsupervised Image Decomposition via Coupled Deep-Image-Priors (2018) — 367 citations