Generalized Continual Category Discovery
Daniel Marczak Grzegorz Rypeść Sebastian Cygert Tomasz Trzciński Bartłomiej Twardowski
Most of Continual Learning (CL) methods push the limit of supervised learning
settings, where an agent is expected to learn new labeled tasks and not forget
previous knowledge. However, these settings are not well aligned with real-life
scenarios, where a learning agent has access to a vast amount of unlabeled data
encompassing both novel (entirely unlabeled) classes and examples from known
classes. Drawing inspiration from Generalized Category Discovery (GCD), we
introduce a novel framework that relaxes this assumption. Precisely, in any
task, we allow for the existence of novel and known classes, and one must use
continual version of unsupervised learning methods to discover them. We call
this setting Generalized Continual Category Discovery (GCCD). It unifies CL and
GCD, bridging the gap between synthetic benchmarks and real-life scenarios.
With a series of experiments, we present that existing methods fail to
accumulate knowledge from subsequent tasks in which unlabeled samples of novel
classes are present. In light of these limitations, we propose a method that
incorporates both supervised and unsupervised signals and mitigates the
forgetting through the use of centroid adaptation. Our method surpasses strong
CL methods adopted for GCD techniques and presents a superior representation
learning performance.