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.

通过多级分层标签扩展的在线学习约束，我们提出了一种新的多级分级类增量任务配置，该配置允许网络首先学习粗粒化类别，并在各个层次深度中不断扩展到更精细粒度的类别。通过利用层次感知的伪标签技术，结合简单而有效的记忆管理和采样策略，我们的方法能够在所有层次的分层结构中显著提高分类准确性，无论层次深度和类别不平衡比例如何，并且在传统的不相交、模糊和 i-Blurry 的连续学习设置上也优于现有的最先进方法。