Unlabeled data is abundant in industry, but ground truth is scarce. Trafikverket captures hundreds of terabytes of high-resolution imagery annually to monitor Sweden’s railway network. The sheer volume of this data renders manual labeling—and therefore traditional supervised learning—unfeasible.
This talk explores how we are overcoming the "labeling bottleneck" by deploying a self-supervised foundation model trained on this massive, unlabeled archive. We will bridge the gap between theory and practice, starting with a comparison of Self-Supervised Learning (SSL) paradigms. Moving beyond theory, we will demonstrate the practical application of SSL in a large-scale industrial setting.
Attendees will learn how Trafikverket leverages SSL to learn robust visual representations, enabling the fine-tuning of models for critical downstream tasks—such as detecting rail cracks and identifying key infrastructure assets—with minimal labeled data.
Edvin Listo Zec is a Senior Machine Learning Engineer at Eghed, where he applies advanced deep learning to critical business challenges. He holds a PhD from KTH Royal Institute of Technology, where his research focused on distributed deep learning. Previously, Edvin served as a Research Scientist at RISE Research Institutes of Sweden and a Visiting Researcher at NYU. With a background spanning representation learning and out-of-distribution generalization, he is dedicated to bridging the gap between theoretical research and impactful, sustainable applications in the physical world.
Mladen Gibanica is a Senior Data Scientist at eghed, working primarily with Trafikverket to automate railway maintenance using computer vision. He was previously at Volvo Cars, initially as an industrial PhD student, and later as a data scientist working on projects from different domains.
Mladen holds a PhD in Applied Mechanics and has co-founded Ingenjörsarbete För Klimatet, a non-profit organisation conducting engineering work for a sustainable civilisation.
