  Continual learning (CL) addresses the problem of catastrophic forgetting in
neural networks, which occurs when a trained model tends to overwrite
previously learned information, when presented with a new task. CL aims to
instill the lifelong learning characteristic of humans in intelligent systems,
making them capable of learning continuously while retaining what was already
learned. Current CL problems involve either learning new domains
(domain-incremental) or new and previously unseen classes (class-incremental).
However, general learning processes are not just limited to learning
information, but also refinement of existing information. In this paper, we
define CLEO - Continual Learning of Evolving Ontologies, as a new incremental
learning setting under CL to tackle evolving classes. CLEO is motivated by the
need for intelligent systems to adapt to real-world ontologies that change over
time, such as those in autonomous driving. We use Cityscapes, PASCAL VOC, and
Mapillary Vistas to define the task settings and demonstrate the applicability
of CLEO. We highlight the shortcomings of existing CIL methods in adapting to
CLEO and propose a baseline solution, called Modelling Ontologies (MoOn). CLEO
is a promising new approach to CL that addresses the challenge of evolving
ontologies in real-world applications. MoOn surpasses previous CL approaches in
the context of CLEO.
