Robert Geirhos

4.6k total citations · 1 hit paper
12 papers, 1.2k citations indexed

About

Robert Geirhos is a scholar working on Cognitive Neuroscience, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Robert Geirhos has authored 12 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Cognitive Neuroscience, 5 papers in Computer Vision and Pattern Recognition and 4 papers in Artificial Intelligence. Recurrent topics in Robert Geirhos's work include Face Recognition and Perception (5 papers), Visual Attention and Saliency Detection (5 papers) and Visual perception and processing mechanisms (2 papers). Robert Geirhos is often cited by papers focused on Face Recognition and Perception (5 papers), Visual Attention and Saliency Detection (5 papers) and Visual perception and processing mechanisms (2 papers). Robert Geirhos collaborates with scholars based in Germany, Switzerland and United Kingdom. Robert Geirhos's co-authors include Felix A. Wichmann, Matthias Bethge, Claudio Michaelis, Wieland Brendel, Jörn-Henrik Jacobsen, Richard S. Zemel, Heiko H. Schütt, Marianne Maertens, Jonas Rauber and R. Zimmermann and has published in prestigious journals such as Journal of Vision, Nature Machine Intelligence and Annual Review of Vision Science.

In The Last Decade

Robert Geirhos

11 papers receiving 1.1k 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.

Shortcut learning in deep neural networks

2020 833 citations Robert Geirhos, Jörn-Henrik Jacobsen et al. Nature Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Robert Geirhos Germany	5	623	410	151	125	62	12	1.2k
Claudio Michaelis Germany	4	605 1.0×	387 0.9×	122 0.8×	122 1.0×	62 1.0×	4	1.1k
Jörn-Henrik Jacobsen Canada	4	530 0.9×	249 0.6×	72 0.5×	101 0.8×	64 1.0×	6	891
Jose L. Part United Kingdom	5	1.2k 1.8×	542 1.3×	122 0.8×	103 0.8×	39 0.6×	12	1.7k
Simone Palazzo Italy	16	202 0.3×	535 1.3×	145 1.0×	152 1.2×	68 1.1×	79	1.3k
Veronika Cheplygina Netherlands	14	593 1.0×	464 1.1×	48 0.3×	381 3.0×	92 1.5×	24	1.3k
Zifan Wang United States	9	449 0.7×	331 0.8×	44 0.3×	145 1.2×	35 0.6×	18	912
Aravindh Mahendran United States	6	585 0.9×	766 1.9×	87 0.6×	67 0.5×	22 0.4×	9	1.3k
Chaofan Chen China	12	768 1.2×	236 0.6×	37 0.2×	97 0.8×	98 1.6×	40	1.3k
Sirui Ding China	5	422 0.7×	320 0.8×	43 0.3×	138 1.1×	43 0.7×	11	820

Countries citing papers authored by Robert Geirhos

Since Specialization

Citations

This map shows the geographic impact of Robert Geirhos'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 Robert Geirhos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Robert Geirhos more than expected).

Fields of papers citing papers by Robert Geirhos

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert Geirhos. 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 Robert Geirhos. The network helps show where Robert Geirhos may publish in the future.

Co-authorship network of co-authors of Robert Geirhos

This figure shows the co-authorship network connecting the top 25 collaborators of Robert Geirhos. A scholar is included among the top collaborators of Robert Geirhos 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 Robert Geirhos. Robert Geirhos is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

12 of 12 papers shown

1.

Geirhos, Robert, et al.. (2023). The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks. Journal of Vision. 23(7). 4–4. 14 indexed citations

2.

Wichmann, Felix A. & Robert Geirhos. (2023). Are Deep Neural Networks Adequate Behavioral Models of Human Visual Perception?. Annual Review of Vision Science. 9(1). 501–524. 28 indexed citations

3.

Geirhos, Robert, et al.. (2022). Beyond Neural Scaling Laws: Beating Power Law Scaling Via Data Pruning. 19523–19536.

4.

Geirhos, Robert, et al.. (2022). The bittersweet lesson: data-rich models narrow the behavioural gap to human vision. Journal of Vision. 22(14). 3273–3273. 1 indexed citations

5.

Geirhos, Robert, et al.. (2021). The developmental trajectory of object recognition robustness: comparing children, adults, and CNNs. Journal of Vision. 21(9). 1967–1967. 2 indexed citations

6.

Geirhos, Robert, Jörn-Henrik Jacobsen, Claudio Michaelis, et al.. (2020). Shortcut learning in deep neural networks. Nature Machine Intelligence. 2(11). 665–673. 833 indexed citations breakdown →

7.

Zimmermann, R., et al.. (2020). Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization. arXiv (Cornell University). 2 indexed citations

8.

Zimmermann, R., et al.. (2020). Natural Images are More Informative for Interpreting CNN Activations than State-of-the-Art Synthetic Feature Visualizations. 1 indexed citations

9.

Geirhos, Robert, Jonas Rauber, Claudio Michaelis, et al.. (2019). Inducing a human-like shape bias leads to emergent human-level distortion robustness in CNNs. Journal of Vision. 19(10). 209c–209c. 3 indexed citations

10.

Geirhos, Robert, et al.. (2018). ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. arXiv (Cornell University). 265 indexed citations

11.

Geirhos, Robert, et al.. (2017). Of Human Observers and Deep Neural Networks: A Detailed Psychophysical Comparison. Journal of Vision. 17(10). 806–806. 1 indexed citations

12.

Wichmann, Felix A., et al.. (2017). Methods and measurements to compare men against machines. Electronic Imaging. 29(14). 36–45. 14 indexed citations

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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