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.
