Elnaz Barshan
Impact in
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- Face and Expression Recognition
- Image Retrieval and Classification Techniques
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- Neural Networks and Applications
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
Papers in
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- Generative Adversarial Networks and Image Synthesis 2
- Advanced Neural Network Applications 2
- Face and Expression Recognition 1
- Image and Signal Denoising Methods 1
- Advanced Steganography and Watermarking Techniques 1
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- Domain Adaptation and Few-Shot Learning 2
- Neural Networks and Applications 1
- Co-authors
- Mansoor Zolghadri Jahromi (1 shared paper)Zohreh Azimifar (1 shared paper)Ali Ghodsi (1 shared paper)Paul Fieguth (4 shared papers)Gintare Karolina Dziugaite (1 shared paper)Christian Scharfenberger (1 shared paper)Alexander Wong (1 shared paper)Feng Yang (1 shared paper)
- Journals
- Pattern Recognition (1 paper)SID Symposium Digest of Technical Papers (1 paper)International Conference on Artificial Intelligence and Statistics (1 paper)Neural Information Processing Systems (1 paper)Electronic Imaging (1 paper)
In The Last Decade
Elnaz Barshan
7 papers receiving 227 citations
Peers
Comparison fields: 5 of 91
- Computer Vision and Pattern Recognition 92
- Artificial Intelligence 87
- Analytical Chemistry 25
- Media Technology 20
- Signal Processing 23
Countries citing papers authored by Elnaz Barshan
This map shows the geographic impact of Elnaz Barshan'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 Elnaz Barshan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Elnaz Barshan more than expected).
Fields of papers citing papers by Elnaz Barshan
This network shows the impact of papers produced by Elnaz Barshan. 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 Elnaz Barshan. The network helps show where Elnaz Barshan may publish in the future.
Co-authors
The 10 scholars most cited alongside Elnaz Barshan, 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 | 2010 | 201 | |
| 2 | Stage-wise Training: An Improved Feature Learning Strategy for Deep Models | 2015 | 11 |
| 3 | RelatIF: Identifying Explanatory Training Samples via Relative Influence | 2020 | 10 |
| 4 | 2015 | 6 | |
| 5 | 2023 | 4 | |
| 6 | 2015 | 2 | |
| 7 | 2014 | 1 |
About Elnaz Barshan
Elnaz Barshan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology, Molecular Biology and Computational Mechanics, having authored 7 papers that have together received 235 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (2 papers), Advanced Neural Network Applications (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Face and Expression Recognition (1 paper), Neural Networks and Applications (1 paper), Image and Signal Denoising Methods (1 paper), Spectroscopy and Chemometric Analyses (1 paper) and Advanced Steganography and Watermarking Techniques (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (92 citations), Artificial Intelligence (87 citations), Analytical Chemistry (25 citations), Media Technology (20 citations) and Signal Processing (23 citations). Elnaz Barshan has collaborated with scholars based in Canada, Iran and Germany. Frequent co-authors include Mansoor Zolghadri Jahromi, Zohreh Azimifar, Ali Ghodsi, Paul Fieguth, Gintare Karolina Dziugaite, Christian Scharfenberger, Alexander Wong, Feng Yang, Xiyang Luo and Michael E. Goebel. Their work appears in journals such as Pattern Recognition, SID Symposium Digest of Technical Papers, International Conference on Artificial Intelligence and Statistics, Neural Information Processing Systems and Electronic Imaging.
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.