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Hakan Bilen

Hakan Bilen has 2 papers in our library, in 2017–2020; often with Andrea Vedaldi, Bo Zhao.

2 papers in our library · 2017–2020

OpenAlex · ORCID · h-index 28 · 116 works · 4,708 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 280 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #3013

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of EdinburghEdinburgh · United Kingdom2016–2026 · 74 papersEmployment 2017–2022 · Lecturer, School of InformaticsEmployment since 2022 · Readercurrent🎓University of OxfordOxford · United Kingdom2015–2018 · 9 papersEmployment 2015–2017 · Postdoctoral Researcher, Department of Engineering Science🎓KU LeuvenLeuven · Belgium2008–2014 · 7 papersEducation 2008–2013 · PhD🎓Sabancı ÜniversitesiIstanbul · Türkiye2007–2010 · 5 papers🤝iMindsGhent · Belgium2013–2015 · 3 papers
🏛️Sabanci UniversitesiTuzla · Türkiye2006–2008Education 2006–2008 · Master of Science, Department of Science and Engineering

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 works

💡 Concepts 1Key ideas and methods explained on our concept pages.Fine-tuning · 1 work

 Works by year

2020s

20201 paper

2010s

20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Learning multiple visual domains with residual adapters (2017 · 694 citations)
    The paper addresses multiple-domain learning. The goal is one image representation that serves very different kinds of pictures, from ImageNet photos to traffic signs, handwritten glyphs, textures and digits. It should not need a separate network per domain, and it should not lose old domains when new ones are added.
  2. Dataset Condensation with Gradient Matching (2020 · 159 citations)
    Modern deep learning depends on ever-larger training sets, which makes storing data and training models costly.

All works

5 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Learning multiple visual domains with residual adaptersarXiv:1705.08045Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi2017 · 694 citations
  2. Dynamic Image Networks for Action Recognition ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetHakan Bilen et al.2016 · 584 citations
  3. Self-Supervised Video Representation Learning with Odd-One-Out Networks ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetBasura Fernando et al.2017 · 435 citations
  4. Weakly supervised object detection with convex clustering ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetHakan Bilen, Marco Pedersoli, Tinne Tuytelaars2015 · 210 citations
  5. Dataset Condensation with Gradient MatchingInternational Conference on Learning Representations (ICLR)Bo Zhao, Konda Reddy Mopuri, Hakan Bilen2020 · 159 citations

 Co-authors here

2 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 280 times (of the 149,439 papers whose bibliographies we read); 35 citing papers with a page here →.

Updated

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