In many real-world vision systems, performance is limited less by model architecture than by the cost and quality of the data. This talk introduces a practical approach for "min-maxing" your data: maximizing expressibility while minimizing sample count through information-based subset selection. Whether choosing calibration frames or deciding which images deserve annotation, selecting the most representative samples can lead to faster results, lower labeling effort, and more accurate models, especially when building few-shot or data-efficient pipelines.
Taigatech is a Gothenburg-based startup that is making sawmills around the world more efficient with AI. Deployments are fast-paced, and AI models need to uphold quality quickly. In this context, smart data selection is not an academic luxury but a necessity for quick deliveries and maintainable neural networks.
Drawing on Taigatech's work in computer vision, I'll show how these techniques improve reliability and efficiency in production systems where data collection is expensive and conditions are harsh. Attendees will learn simple, deployable strategies for using their data more efficiently and building CV systems that perform better with less.
Mattias first encountered machine learning and computer vision in my bachelor's thesis when studying Engineering Physics at Chalmers. Fascinated by the subject, he continued by studying Complex Adaptive Systems, specializing in machine learning.
Since then, he has worked in computer vision throughout his career, including serving as Taigatech's first hire and helping design multiple vision engines from the ground up. These products are used worldwide, improving yields and sustainability.
Beyond his professional work, he pursues several personal projects in machine learning, with a particular interest in reinforcement learning.
