World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
67
Citations
35570
World Ranking
2137
National Ranking
89

Matthias Bethge publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Matthias Bethge sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 331 publications — 79th percentile

79% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Matthias Bethge D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Matthias Bethge sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 67 D-Index — 85th percentile

85% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Matthias Bethge is affiliated with the University of Tübingen in Germany and has a research portfolio spanning computer science and health professions. Their work integrates multiple fields of study including artificial intelligence, computer vision and pattern recognition, cognitive neuroscience, and physical therapy, sports therapy, and rehabilitation.

The scientist has contributed extensively to research topics such as workplace health and well-being, medical practices and rehabilitation, visual attention and saliency detection, musculoskeletal pain and rehabilitation, domain adaptation and few-shot learning, health and medical studies, along with neural dynamics and brain function.

Frequent coauthors collaborating with Matthias Bethge include Wieland Brendel, Matthias Kümmerer, David Fauser, Thomas S. A. Wallis, and Wilfried Mau.

Their research has been published in multiple venues, with notable publication counts in arXiv (Cornell University), Die Rehabilitation, Zenodo (CERN European Organization for Nuclear Research), Journal of Vision, and bioRxiv (Cold Spring Harbor Laboratory).

Significant recent papers authored or coauthored by Matthias Bethge include:

  • Shortcut learning in deep neural networks, 2020, Nature Machine Intelligence
  • Improving robustness against common corruptions by covariate shift adaptation, 2020, arXiv (Cornell University)
  • Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX, 2020, The Journal of Open Source Software
  • DeepGaze III: Modeling free-viewing human scanpaths with deep learning, 2022, Journal of Vision
  • DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modeling, 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV)

Best Publications

  • Image Style Transfer Using Convolutional Neural Networks

    Leon A. Gatys;Alexander S. Ecker;Matthias Bethge

  • DeepLabCut: markerless pose estimation of user-defined body parts with deep learning

    Alexander Mathis;Pranav Mamidanna;Kevin M. Cury;Taiga Abe

  • A Neural Algorithm of Artistic Style

    Leon A. Gatys;Alexander S. Ecker;Matthias Bethge

  • Shortcut learning in deep neural networks

    Robert Geirhos;Jörn-Henrik Jacobsen;Claudio Michaelis;Richard S. Zemel

  • Using DeepLabCut for 3D markerless pose estimation across species and behaviors

    Tanmay Nath;Alexander Mathis;An Chi Chen;Amir Patel

  • ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

    Robert Geirhos;Patricia Rubisch;Claudio Michaelis;Matthias Bethge

  • Texture synthesis using convolutional neural networks

    Leon A. Gatys;Alexander S. Ecker;Matthias Bethge

  • Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models

    Wieland Brendel;Jonas Rauber;Matthias Bethge

  • A note on the evaluation of generative models

    Lucas Theis;Aäron van den Oord;Matthias Bethge

  • Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq

    Cathryn R Cadwell;Athanasia Palasantza;Athanasia Palasantza;Xiaolong Jiang;Philipp Berens

  • Foolbox v0.8.0: A Python toolbox to benchmark the robustness of machine learning models

    Jonas Rauber;Wieland Brendel;Matthias Bethge

  • Controlling Perceptual Factors in Neural Style Transfer

    Leon A. Gatys;Alexander S. Ecker;Matthias Bethge;Aaron Hertzmann

  • Generalisation in humans and deep neural networks

    Robert Geirhos;Carlos R. Medina Temme;Jonas Rauber;Heiko H. Schütt

  • State dependence of noise correlations in macaque primary visual cortex

    Alexander S. Ecker;Philipp Berens;Philipp Berens;R. James Cotton;Manivannan Subramaniyan

  • Deep convolutional models improve predictions of macaque V1 responses to natural images.

    Santiago A. Cadena;Santiago A. Cadena;George H. Denfield;Edgar Y. Walker;Leon A. Gatys

  • Deep Gaze I: Boosting Saliency Prediction with Feature Maps Trained on ImageNet

    Matthias Kümmerer;Lucas Theis;Matthias Bethge

  • The Effect of Noise Correlations in Populations of Diversely Tuned Neurons

    Alexander S. Ecker;Philipp Berens;Andreas S. Tolias;Matthias Bethge

  • DeepGaze II: Reading fixations from deep features trained on object recognition.

    Matthias Kümmerer;Thomas S. A. Wallis;Matthias Bethge

  • Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet

    Wieland Brendel;Matthias Bethge

  • Inhibition decorrelates visual feature representations in the inner retina

    Katrin Franke;Philipp Berens;Timm Schubert;Matthias Bethge

  • Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming

    Claudio Michaelis;Benjamin Mitzkus;Robert Geirhos;Evgenia Rusak

  • Improving robustness against common corruptions by covariate shift adaptation

    Steffen Schneider;Evgenia Rusak;Luisa Eck;Oliver Bringmann

  • Markerless tracking of user-defined features with deep learning

    Alexander Mathis;Pranav Mamidanna;Taiga Abe;Kevin M. Cury

Frequent Co-Authors

Andreas S. Tolias
Andreas S. Tolias Baylor College of Medicine
Jakob H. Macke
Jakob H. Macke Max Planck Institute for Intelligent Systems
Felix A. Wichmann
Felix A. Wichmann University of Tübingen
Thomas Euler
Thomas Euler University of Tübingen
Alexander Mathis
Alexander Mathis École Polytechnique Fédérale de Lausanne
Matthias Seeger
Matthias Seeger Amazon (Germany)
Manfred Opper
Manfred Opper Technical University of Berlin
Rachel Mandelbaum
Rachel Mandelbaum Carnegie Mellon University
Konrad Kuijken
Konrad Kuijken Leiden University
Catherine Heymans
Catherine Heymans University of Edinburgh

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