Learning Effective Representations for Retrieval Using Self-Distillation with Adaptive Relevance Margins.
Lukas Gienapp|Niklas Deckers|Martin Potthast|Harrisen Scells
| Anthology ID: | DBLP:conf/ictir/GienappDPS25 |
|---|---|
| Volume: | Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval, ICTIR 2025, Padua, Italy, 18 July 2025 |
| Year: | 2025 |
| Venue: | International Conference on the Theory of Information Retrieval (ICTIR) |
| Publisher: | ACM |
| Pages: | 275-285 |
| URL: | https://doi.org/10.1145/3731120.3744594 |
| DOI: | https://doi.org/10.1145/3731120.3744594 |
| DBLP: | conf/ictir/GienappDPS25 |
| BibTeX: |
@inproceedings{gienapp-2025-learning,
author = {Lukas Gienapp and
Niklas Deckers and
Martin Potthast and
Harrisen Scells},
editor = {Hamed Zamani and
Laura Dietz and
Benjamin Piwowarski and
Sebastian Bruch},
title = {{Learning Effective Representations for Retrieval Using Self-Distillation with Adaptive Relevance Margins}},
booktitle = {{Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval, ICTIR 2025, Padua, Italy, 18 July 2025}},
pages = {275--285},
publisher = {ACM},
year = {2025},
url = {https://doi.org/10.1145/3731120.3744594},
doi = {https://doi.org/10.1145/3731120.3744594}
}