Fabian Mentzer
Computer Vision and Pattern Recognition · 2017–2025
A researcher in the Science corpus: 9 papers; trusts 621 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 202 citations into it; #6,245 of 21,575 by trust here, #5,205 by citations
- Artificial Intelligence — 73 citations into it
- Generative Models — 73 citations into it; #954 of 1,956 by trust here, #973 by citations
- Efficiency & Compression — 33 citations into it; #825 of 1,264 by trust here, #673 by citations
- Signal Processing — 29 citations into it
- Media Technology — 28 citations into it
- Optimization & Training — 28 citations into it
- Evaluation & Benchmarks — 25 citations into it
Whom Fabian Mentzer trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Alex Krizhevsky
- Sergey Ioffe
- Eirikur Agustsson
- Geoffrey E. Hinton
- Kaiming He
- Yoshua Bengio
- Johannes Ballé
- Karen Simonyan
- Aäron van den Oord
Who trusts Fabian Mentzer overall
- Eirikur Agustsson
- Michael Tschannen
- David C. Minnen
- Yochai Blau
- Renjie Zou
- Guo Lu
- Tong Chen
- Shifeng Zhang
- AmirHossein Habibian
- Liyuan Wang
Papers from Fabian Mentzer's perspective
Ordered by how strongly the researchers Fabian Mentzer trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Generative Adversarial Networks for Extreme Learned Image Compression (2018) — 667 citations
- Conditional Probability Models for Deep Image Compression (2018) — 540 citations
- Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations (2017) — 535 citations
- High-Fidelity Generative Image Compression (2020) — 640 citations
- Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
- Finite Scalar Quantization: VQ-VAE Made Simple (2023) — 523 citations
- Practical Full Resolution Learned Lossless Image Compression (2018) — 248 citations
- Soft-to-Hard Vector Quantization for End-to-End Learned Compression of Images and Neural Networks (2017) — 46 citations