Michael Tschannen

25 papers receiving 480 citations

Peers

Michael Tschannen
Comparison fields: 5 of 87
  • Computer Vision and Pattern Recognition 276
  • Artificial Intelligence 198
  • Signal Processing 82
  • Pulmonary and Respiratory Medicine 74
  • Radiology, Nuclear Medicine and Imaging 38
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Countries citing papers authored by Michael Tschannen

Since Specialization
Citations

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

Fields of papers citing papers by Michael Tschannen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Tschannen

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 43
3 9
4
On Mutual Information Maximization for Representation Learning
15
5
Disentangling Factors of Variations Using Few Labels
21
6
Automatic Shortcut Removal for Self-Supervised Representation Learning
2
7
Weakly-Supervised Disentanglement Without Compromises
4
8
High-Fidelity Image Generation With Fewer Labels
16
9
The Visual Task Adaptation Benchmark
22
10
Born Again Neural Networks
109
11
Towards Image Understanding from Deep Compression without Decoding
4
12
Extreme Learned Image Compression with GANs
7
13
StrassenNets: Deep Learning with a Multiplication Budget.
2
14
Soft-to-Hard Vector Quantization for End-to-End Learned Compression of Images and Neural Networks.
10
15
Soft-to-hard vector quantization for end-to-end learning compressible representations
77
16 8
17 2
18 13
19 73
20
Single Marker Localization for Automatic Patient Registration in Interventional Radiology
2

About Michael Tschannen

Michael Tschannen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biophysics, having authored 26 papers that have together received 502 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (6 papers), Advanced Neural Network Applications (5 papers) and Domain Adaptation and Few-Shot Learning (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (276 citations), Signal Processing (82 citations) and Artificial Intelligence (198 citations). Michael Tschannen has collaborated with scholars based in Switzerland, United States and Germany. Frequent co-authors include Zachary C. Lipton, Anima Anandkumar, Tommaso Furlanello, Laurent Itti, Fabian Mentzer, Luc Van Gool, Luca Benini, Lukas Cavigelli, Radu Timofte and Eirikur Agustsson. Their work appears in journals such as IEEE Transactions on Signal Processing, International Journal of Radiation Oncology*Biology*Physics and Medical Image Analysis.

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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