Philip Haeusser

621 total citations
3 papers, 274 citations indexed

About

Philip Haeusser is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Philip Haeusser has authored 3 papers receiving a total of 274 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Computer Vision and Pattern Recognition, 2 papers in Artificial Intelligence and 1 paper in Atomic and Molecular Physics, and Optics. Recurrent topics in Philip Haeusser's work include Domain Adaptation and Few-Shot Learning (2 papers), Multimodal Machine Learning Applications (1 paper) and Human Pose and Action Recognition (1 paper). Philip Haeusser is often cited by papers focused on Domain Adaptation and Few-Shot Learning (2 papers), Multimodal Machine Learning Applications (1 paper) and Human Pose and Action Recognition (1 paper). Philip Haeusser collaborates with scholars based in United States, Taiwan and Germany. Philip Haeusser's co-authors include Daniel Cremers, Alexander Mordvintsev, Huan‐Cheng Chang, David Hunger, Helmut Fedder, Theodor W. Hänsch and Thomas Hümmer and has published in prestigious journals such as Physical Review Applied.

In The Last Decade

Philip Haeusser

3 papers receiving 263 citations

Peers

Philip Haeusser
Lingsheng Kong China
ByungIn Yoo South Korea
T. Yasuda Japan
Masashi Ito Japan
Cory Stephenson United States
D. Vijayalakshmi India
Ralph Breithaupt Germany
Rolf‐Rainer Grigat Germany
Lingsheng Kong China
Philip Haeusser
Citations per year, relative to Philip Haeusser Philip Haeusser (= 1×) peers Lingsheng Kong

Countries citing papers authored by Philip Haeusser

Since Specialization
Citations

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

Fields of papers citing papers by Philip Haeusser

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philip Haeusser

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

All Works

3 of 3 papers shown
1.
Haeusser, Philip, Alexander Mordvintsev, & Daniel Cremers. (2017). Learning by Association — A Versatile Semi-Supervised Training Method for Neural Networks. 626–635. 58 indexed citations
2.
Haeusser, Philip, et al.. (2017). Associative Domain Adaptation. 2784–2792. 143 indexed citations
3.
Hümmer, Thomas, Philip Haeusser, Huan‐Cheng Chang, et al.. (2016). Purcell-Enhanced Single-Photon Emission from Nitrogen-Vacancy Centers Coupled to a Tunable Microcavity. Physical Review Applied. 6(5). 73 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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