Adam Kortylewski

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 516 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 115 citations into it; #20,507 of 21,575 by trust here, #15,162 by citations
• Artificial Intelligence — 100 citations into it; #33,616 of 35,345 by trust here, #23,077 by citations
• Model Architectures — 23 citations into it; #2,193 of 2,278 by trust here, #2,078 by citations
• Optimization & Training — 22 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Meta & Continual Learning — 18 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Safety, Robustness & Interpretability — 13 citations into it

Whom Adam Kortylewski trusts overall

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

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

Who trusts Adam Kortylewski overall

1. Jiyang Qi
2. Yan Gao
3. Alan L. Yuille
4. Qing Liu
5. Yao Hu

Papers from Adam Kortylewski's perspective

Ordered by how strongly the researchers Adam Kortylewski 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• Compositional Convolutional Neural Networks: A Deep Architecture With Innate Robustness to Partial Occlusion (2020) — 110 citations
• Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition Under Occlusion (2020) — 90 citations
• Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion (2019) — 74 citations
• Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping (2021) — 80 citations
• TransFG: A Transformer Architecture for Fine-grained Recognition (2021) — 559 citations