Luis Herranz

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 13 papers; trusts 1,079 researchers and is trusted by 106 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 321 citations into it; #3,094 of 21,575 by trust here, #1,827 by citations
• Artificial Intelligence — 272 citations into it; #4,933 of 35,345 by trust here, #2,453 by citations
• Representation & Self-Supervised Learning — 107 citations into it
• Generative Models — 78 citations into it; #689 of 1,956 by trust here, #432 by citations
• Meta & Continual Learning — 54 citations into it; #133 of 1,041 by trust here, #52 by citations
• Optimization & Training — 44 citations into it
• Evaluation & Benchmarks — 37 citations into it
• Radiology, Nuclear Medicine and Imaging — 37 citations into it

Whom Luis Herranz trusts overall

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

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

Who trusts Luis Herranz overall

1. Marc Masana
2. Fei Ye
3. Xialei Liu
4. Liyuan Wang
5. Da-Wei Zhou
6. Joost van de Weijer
7. Bartłomiej Twardowski
8. Simon Kornblith
9. Rahaf Aljundi
10. Timothée Lesort

Papers from Luis Herranz's perspective

Ordered by how strongly the researchers Luis Herranz 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. 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. Going deeper with convolutions

Their papers

• Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting (2018) — 327 citations
• Semantic Drift Compensation for Class-Incremental Learning (2020) — 422 citations
• Transferring GANs: generating images from limited data (2018) — 320 citations
• MineGAN: Effective Knowledge Transfer From GANs to Target Domains With Few Images (2019) — 208 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
• Domain-Adaptive Deep Network Compression (2017) — 64 citations
• Scene Recognition with CNNs: Objects, Scales and Dataset Bias (2016) — 225 citations
• LIUM-CVC Submissions for WMT17 Multimodal Translation Task (2017) — 71 citations
• Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation (2021) — 351 citations
• ACAE-REMIND for Online Continual Learning with Compressed Feature Replay (2021) — 21 citations
• Generalized Source-free Domain Adaptation (2021) — 328 citations
• Bookworm continual learning: beyond zero-shot learning and continual learning (2020) — 6 citations