Unit Jena: ELLIS Summer Lecture Series - Explainability & Understanding of Models

The ELLIS Summer Lecture Series on “Explainability & Understanding of Models” brings together leading researchers to explore one of the most pressing challenges in modern AI: making complex machine learning systems transparent, interpretable, and trustworthy. As models grow in scale and impact, understanding how and why they make decisions becomes essential for scientific progress, real-world deployment, and societal acceptance. This lecture series offers cutting-edge insights into methods for explaining model behavior, uncovering internal representations, and bridging the gap between performance and interpretability—providing a platform for discussion, learning, and collaboration across the ELLIS community and beyond.

The lecture series is supported by the Professorinnenprogramm 2030 funded by the German federal and state governments as well as by the “Women in ELLIS” initiative. The Professorinnenprogramm 2030 promotes gender equality and supports the advancement of women in academia and research institutions across Germany. As part of the Professorinnenprogramm 2030, Friedrich Schiller University Jena received a positive evaluation of its gender equality concept by an independent review panel and was recognized as a “University with Strong Gender Equality” (“gleichstellungsstarke Hochschule”). “Women in ELLIS” highlights and connects leading female researchers in artificial intelligence and machine learning across Europe. Through lectures, mentoring, and networking activities, the initiative aims to strengthen diversity and female leadership in AI research.

TITLE
Understanding Deep Vision Models and Its Benefits

ABSTRACT
Deep learning has led to remarkable progress in computer vision, yet benchmark accuracy alone provides only a limited view of model capabilities. In this talk, I will argue that a deeper analysis of model behavior can yield both practical improvements and conceptual insights. First, I will show how fine-grained performance analyses can lead towards simpler and more computationally efficient vision models. In the second part, I will focus on understanding model behavior through visual explanations. I will discuss the role of visual explanations in improving classification and present a method to obtain such explanations efficiently in practice. I will then highlight recent findings showing that deep networks rely not only on the presence of visual concepts, but also on their absence and show how extending attribution and feature visualization methods makes these effects visible. Together, these perspectives illustrate how a deeper understanding of model behavior can inform the design of more efficient, reliable, and interpretable vision models. 

BIO
Simone Schaub-Meyer leads a research group on image and video analysis at the Technical University of Darmstadt. Her work focuses on developing efficient, robust, and interpretable methods for visual perception Her research is supported by the Emmy Noether Programme of the German Research Foundation. Assistant Professor Schaub-Meyer is a member of the ELLIS Unit Darmstadt and the Hessian Center for Artificial Intelligence (hessian.AI). She received her doctoral degree from ETH Zurich in collaboration with Disney Research Zurich, where her thesis on motion representation and video frame interpolation was awarded the ETH Medal.