Jakub Lengiewicz

Abstract: Convolutional neural networks (CNNs) are increasingly being used in critical systems, where robustness and alignment are crucial. In this context, the field of explainable artificial intelligence has proposed the generation of high-level explanations of the prediction process of CNNs through concept extraction. While these methods can detect whether or not a concept is present in an image, they are unable to determine its location. What is more, a fair comparison of such approaches is difficult due to a lack of proper validation procedures. In this talk, we discuss a novel method for automatic concept extraction and localization based on representations obtained through pixel-wise aggregations of CNN activation maps. Further, we introduce a process for the validation of concept-extraction techniques based on synthetic datasets with pixel-wise annotations of their main components, reducing the need for human intervention.

Andres Posada Moreno is a PhD Student, Institute for Data Science in Mechanical Engineering at the RWTH Aachen University. His expertise lies in research and applied technologies in multidisciplinary environments, with a focus on artificial intelligence. He is  experienced in the research, development, and management of AI projects in agriculture, manufacturing quality control, robotics, and railway systems. His current research focuses in the field of explainable artificial intelligence.