B. Raducanu

Computer Vision and Pattern Recognition · 2016–2022

A researcher in the Science corpus: 5 papers; trusts 333 researchers and is trusted by 60 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 137 citations into it; #3,851 of 21,575 by trust here, #2,938 by citations
• Artificial Intelligence — 99 citations into it; #11,806 of 35,345 by trust here, #6,103 by citations
• Generative Models — 68 citations into it; #434 of 1,956 by trust here, #330 by citations
• Meta & Continual Learning — 43 citations into it; #433 of 1,041 by trust here, #188 by citations
• Representation & Self-Supervised Learning — 42 citations into it
• Model Architectures — 19 citations into it
• Optimization & Training — 17 citations into it
• Evaluation & Benchmarks — 13 citations into it

Whom B. Raducanu trusts overall

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

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

Who trusts B. Raducanu overall

1. Yuval Alaluf
2. Yotam Nitzan
3. Yujun Shen
4. Fei Ye
5. Zhenliang He
6. Jinjin Gu
7. Aliaksandr Siarohin
8. Omer Tov
9. Divya Saxena

Papers from B. Raducanu's perspective

Ordered by how strongly the researchers B. Raducanu 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. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Invertible Conditional GANs for image editing (2016) — 672 citations
• Transferring GANs: generating images from limited data (2018) — 320 citations
• Memory Replay GANs: learning to generate images from new categories without forgetting (2018) — 203 citations
• Generative Feature Replay For Class-Incremental Learning (2020) — 190 citations
• Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization (2022) — 42 citations