PhD defence
Exploring Deep Learning for Data-Efficient and Generalizable Visual Recognition
- K. He
- Date
- Tuesday 22 September 2026
- Time
- Location
-
Academy Building
Rapenburg 73
2311 GJ Leiden
Supervisor(s)
- Prof.dr. M.S.K. Lew
- dr. E.M. Bakker
Summary
This PhD research explores how artificial intelligence (AI) systems learn to recognize new image categories from a limited number of labelled samples. Unlike conventional image classification methods that rely on large training datasets, few-shot learning aims to enable models to learn effectively from scarce data and generalize to previously unseen categories.
The research investigates few-shot learning along three main directions: metric learning, feature enrichment, and knowledge transfer. Through a series of models and experiments, the work explores how AI can better distinguish visually similar categories, build richer and more informative representations from limited data, and make effective use of previously acquired knowledge. Four proposed methods—ProtoSimNet, AFPN, AMFF, and DSKT—demonstrate how these challenges can be addressed in complementary ways.
Overall, this research contributes to a better understanding of how image recognition systems can remain effective when labelled data are limited. By addressing limitations in similarity modelling, feature representation, and knowledge transfer, the work provides several strategies for improving model performance when only a small amount of labelled data is available. These findings contribute to the development of more data-efficient and adaptable AI systems, and may help extend image recognition to applications where collecting large-scale labelled datasets is difficult, costly, or impractical.
PhD dissertations
Approximately one week after the defence, PhD dissertations by Leiden PhD students are available digitally through the Leiden Repository, that offers free access to these PhD dissertations. Please note that in some cases a dissertation may be under embargo temporarily and access to its full-text version will only be granted later.
Press enquiries (journalists only)
+31 (0)71 527 1521
nieuws@leidenuniv.nl
General information
Beadle's Office
pedel@bb.leidenuniv.nl
+31 71 527 7211