SyCLeC: Symposium on Continual Learning beyond Classification

A three day in-person symposium from Nov 27th to Nov 29th in Mainz, Germany. Organized by Simone Schaub-Meyer & Martin Mundt, enabled by the Hessian Center for Artificial Intelligence – hessian.AI – Connectom network funding program.

Abstract: Continual, or lifelong, machine learning addresses crucial questions that arise when aiming to overcome the limitations of single training cycles, rigid inference engines, and fixed datasets. At the center, it acknowledges that data selection, models, training algorithms, and evaluation measures are not static. Despite its recently emerged popularity, current research has just started to grasp how we can accommodate these factors into human-like lifelong learning AI systems. Although presently ongoing efforts start to take into account complex sequences of datasets, they focus predominantly on image classification tasks. Unfortunately, such tasks significantly simplify learning, e.g. by only performing image-level recognition, assuming that all data is always labelled and that continual learning consists mainly of learning to recognize new object types. In this initiative, we thus set out to bring together researchers who are at the forefront of continual learning and computer vision to jointly catalyze the foundation for continual learning beyond classification. In an inaugural symposium, we will establish common grounds and discover synergies towards continual learning for a plethora of relevant computer vision tasks, such as semantic segmentation and learning without labels.

Symposium Schedule