  Continual learning (CL) enables models to adapt to new tasks and environments
without forgetting previously learned knowledge. While current CL setups have
ignored the relationship between labels in the past task and the new task with
or without small task overlaps, real-world scenarios often involve hierarchical
relationships between old and new tasks, posing another challenge for
traditional CL approaches. To address this challenge, we propose a novel
multi-level hierarchical class incremental task configuration with an online
learning constraint, called hierarchical label expansion (HLE). Our
configuration allows a network to first learn coarse-grained classes, with data
labels continually expanding to more fine-grained classes in various hierarchy
depths. To tackle this new setup, we propose a rehearsal-based method that
utilizes hierarchy-aware pseudo-labeling to incorporate hierarchical class
information. Additionally, we propose a simple yet effective memory management
and sampling strategy that selectively adopts samples of newly encountered
classes. Our experiments demonstrate that our proposed method can effectively
use hierarchy on our HLE setup to improve classification accuracy across all
levels of hierarchies, regardless of depth and class imbalance ratio,
outperforming prior state-of-the-art works by significant margins while also
outperforming them on the conventional disjoint, blurry and i-Blurry CL setups.
