David Stutz

1.2k total citations · 1 hit paper
15 papers, 600 citations indexed

About

David Stutz is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Electrical and Electronic Engineering. According to data from OpenAlex, David Stutz has authored 15 papers receiving a total of 600 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 3 papers in Electrical and Electronic Engineering. Recurrent topics in David Stutz's work include Adversarial Robustness in Machine Learning (6 papers), Advanced Neural Network Applications (4 papers) and Neural Networks and Applications (2 papers). David Stutz is often cited by papers focused on Adversarial Robustness in Machine Learning (6 papers), Advanced Neural Network Applications (4 papers) and Neural Networks and Applications (2 papers). David Stutz collaborates with scholars based in Germany, United Kingdom and United States. David Stutz's co-authors include Alexander Hermans, Bastian Leibe, Andreas Geiger, Bernt Schiele, Matthias Hein, Yong Guo, Nandhini Chandramoorthy, Marc Oliver Rieger, Rajeev Rikhye and Randal Douc and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Medical Image Analysis and Computer Vision and Image Understanding.

In The Last Decade

David Stutz

13 papers receiving 591 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Superpixels: An evaluation of the state-of-the-art

2017 343 citations David Stutz, Alexander Hermans et al. Computer Vision and Image Understanding profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
David Stutz Germany	9	325	136	118	114	71	15	600
Weiliang Meng China	15	410 1.3×	173 1.3×	206 1.7×	60 0.5×	31 0.4×	84	716
Qicong Wang China	15	293 0.9×	165 1.2×	116 1.0×	66 0.6×	16 0.2×	69	689
Michal Haindl Czechia	15	506 1.6×	119 0.9×	77 0.7×	119 1.0×	220 3.1×	86	730
Edward K. Wong United States	15	713 2.2×	94 0.7×	77 0.7×	178 1.6×	50 0.7×	44	963
Xiaolong Fan China	15	222 0.7×	81 0.6×	133 1.1×	150 1.3×	15 0.2×	31	564
Alex Levinshtein Canada	9	802 2.5×	309 2.3×	76 0.6×	70 0.6×	55 0.8×	16	1.1k
Tinghuai Wang Finland	12	393 1.2×	62 0.5×	55 0.5×	47 0.4×	125 1.8×	30	573
Yakun Ju China	15	420 1.3×	91 0.7×	26 0.2×	118 1.0×	142 2.0×	45	566
Qiulei Dong China	14	302 0.9×	78 0.6×	105 0.9×	199 1.7×	36 0.5×	68	662
Renrui Zhang China	10	453 1.4×	45 0.3×	312 2.6×	92 0.8×	32 0.5×	18	732

Countries citing papers authored by David Stutz

Since Specialization

Citations

This map shows the geographic impact of David Stutz's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Stutz with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Stutz more than expected).

Fields of papers citing papers by David Stutz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Stutz. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Stutz. The network helps show where David Stutz may publish in the future.

Co-authorship network of co-authors of David Stutz

This figure shows the co-authorship network connecting the top 25 collaborators of David Stutz. A scholar is included among the top collaborators of David Stutz based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David Stutz. David Stutz is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

1.

Stutz, David, Ali Taylan Cemgil, Abhijit Guha Roy, et al.. (2025). Evaluating medical AI systems in dermatology under uncertain ground truth. Medical Image Analysis. 103. 103556–103556.

2.

Guo, Yong, David Stutz, & Bernt Schiele. (2023). Robustifying Token Attention for Vision Transformers. 17511–17522. 18 indexed citations

3.

Guo, Yong, David Stutz, & Bernt Schiele. (2023). Improving Robustness of Vision Transformers by Reducing Sensitivity to Patch Corruptions. 4108–4118. 10 indexed citations

4.

Stutz, David, Nandhini Chandramoorthy, Matthias Hein, & Bernt Schiele. (2022). Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(3). 3632–3647. 10 indexed citations

5.

Stutz, David, Nandhini Chandramoorthy, Matthias Hein, & Bernt Schiele. (2021). Bit Error Robustness for Energy-Efficient DNN Accelerators. MPG.PuRe (Max Planck Society). 3. 569–598. 11 indexed citations

6.

Rieger, Marc Oliver, et al.. (2021). Nudging against panic selling: Making use of the IKEA effect. Journal of Behavioral and Experimental Finance. 30. 100502–100502. 4 indexed citations

7.

Stutz, David, Matthias Hein, & Bernt Schiele. (2021). Relating Adversarially Robust Generalization to Flat Minima. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 7787–7797. 15 indexed citations

8.

Stutz, David, Matthias Hein, & Bernt Schiele. (2020). Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks. MPG.PuRe (Max Planck Society). 1. 9155–9166. 9 indexed citations

9.

Stutz, David, Matthias Hein, & Bernt Schiele. (2019). Confidence-Calibrated Adversarial Training and Detection: More Robust Models Generalizing Beyond the Attack Used During Training. arXiv (Cornell University). 2 indexed citations

10.

Stutz, David, Matthias Hein, & Bernt Schiele. (2019). Confidence-Calibrated Adversarial Training: Towards Robust Models Generalizing Beyond the Attack Used During Training. 2 indexed citations

11.

Stutz, David & Andreas Geiger. (2018). Learning 3D Shape Completion from Laser Scan Data with Weak Supervision. 1955–1964. 133 indexed citations

12.

Stutz, David, Alexander Hermans, & Bastian Leibe. (2017). Superpixels: An evaluation of the state-of-the-art. Computer Vision and Image Understanding. 166. 1–27. 343 indexed citations breakdown →

13.

Stutz, David. (2014). Understanding Convolutional Neural Networks. 41 indexed citations

14.

Stutz, David. (2014). Introduction to Neural Networks. 2 indexed citations

15.

Stutz, David, Antônio José da Silva Neto, & Geraldo A. G. Cidade. (2005). Parallel Computation Approach for the Restoration of AFM Imagesbased on the Tikhonov Regularization Method. Microscopy and Microanalysis. 11(S03). 22–25.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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