Arnaud Dapogny

Computer Vision and Pattern Recognition · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 715 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 148 citations into it; #10,780 of 21,575 by trust here, #10,677 by citations
• Artificial Intelligence — 121 citations into it; #17,045 of 35,345 by trust here, #15,866 by citations
• Meta & Continual Learning — 51 citations into it; #532 of 1,041 by trust here, #613 by citations
• Representation & Self-Supervised Learning — 25 citations into it
• Radiology, Nuclear Medicine and Imaging — 21 citations into it
• Optimization & Training — 19 citations into it
• Efficiency & Compression — 14 citations into it
• Evaluation & Benchmarks — 14 citations into it

Whom Arnaud Dapogny trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Ross B. Girshick
5. Yann LeCun
6. Yoshua Bengio
7. Diederik P. Kingma
8. David G. Lowe
9. Ilya Sutskever
10. Sergey Ioffe

Who trusts Arnaud Dapogny overall

1. Arthur Douillard
2. Fabio Cermelli
3. Beomyoung Kim
4. Mathieu Pagé Fortin

Papers from Arnaud Dapogny's perspective

Ordered by how strongly the researchers Arnaud Dapogny trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• PLOP: Learning without Forgetting for Continual Semantic Segmentation (2020) — 319 citations
• Tackling Catastrophic Forgetting and Background Shift in Continual Semantic Segmentation (2021) — 21 citations