Self-supervised and unsupervised learning

Collecting and annotating diverse data is complex, costly, and time-consuming. We explore alternatives to fully-supervised learning, such as unsupervised, self-supervised and active learning, enabling AI models to generate labels automatically and reduce reliance on manually labeled datasets.

Selected publications