Sindy Löwe
Impact in
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- Industrial Vision Systems and Defect Detection
- Artificial Intelligence top 5%
- Anomaly Detection Techniques and Applications
- Adversarial Robustness in Machine Learning
Papers in
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- Industrial Vision Systems and Defect Detection 2
- Manufacturing Process and Optimization 1
- Co-authors
- Paul Bergmann (2 shared papers)Michael Fauser (2 shared papers)David Sattlegger (2 shared papers)Carsten T. Steger (2 shared papers)Bastiaan S. Veeling (1 shared paper)Peter O’Connor (1 shared paper)Francesco Locatello (1 shared paper)Luisa Helena Bartocci Liboni (1 shared paper)
- Journals
- Proceedings of the National Academy of Sciences (1 paper)arXiv (Cornell University) (1 paper)UvA-DARE (University of Amsterdam) (1 paper)
- Partner nations
- NetherlandsGermanyIndia
In The Last Decade
Sindy Löwe
4 papers receiving 551 citations
Sindy Löwe's Hit Papers
Peers
Comparison fields: 5 of 59
- Industrial and Manufacturing Engineering 204
- Artificial Intelligence 419
- Computer Vision and Pattern Recognition 176
- Media Technology 43
- Computer Networks and Communications 99
Countries citing papers authored by Sindy Löwe
This map shows the geographic impact of Sindy Löwe'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 Sindy Löwe with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sindy Löwe more than expected).
Fields of papers citing papers by Sindy Löwe
This network shows the impact of papers produced by Sindy Löwe. 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 Sindy Löwe. The network helps show where Sindy Löwe may publish in the future.
Co-authors
The 12 scholars most cited alongside Sindy Löwe, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders Hit paper breakdown → | 2019 | 330 |
| 2 | 2019 | 220 | |
| 3 | Putting An End to End-to-End: Gradient-Isolated Learning of Representations | 2019 | 13 |
| 4 | 2025 | 2 | |
| 5 | 2023 | 0 |
About Sindy Löwe
Sindy Löwe is a scholar working on Industrial and Manufacturing Engineering, Biophysics, Media Technology, Signal Processing and Artificial Intelligence, having authored 5 papers that have together received 565 indexed citations. Recurring topics across this work include Industrial Vision Systems and Defect Detection (2 papers), Neural Networks and Applications (2 papers), Manufacturing Process and Optimization (1 paper), Neural dynamics and brain function (1 paper), Domain Adaptation and Few-Shot Learning (1 paper), Music and Audio Processing (1 paper), Integrated Circuits and Semiconductor Failure Analysis (1 paper) and Image Processing Techniques and Applications (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (204 citations), Artificial Intelligence (419 citations), Computer Vision and Pattern Recognition (176 citations), Media Technology (43 citations) and Computer Networks and Communications (99 citations). Sindy Löwe has collaborated with scholars based in Netherlands, Germany and India. Frequent co-authors include Paul Bergmann, Michael Fauser, David Sattlegger, Carsten T. Steger, Bastiaan S. Veeling, Peter O’Connor, Francesco Locatello, Luisa Helena Bartocci Liboni, Lyle Muller and Roberto C. Budzinski. Their work appears in journals such as Proceedings of the National Academy of Sciences, arXiv (Cornell University) and UvA-DARE (University of Amsterdam).
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.