# Online Continual Learning on Hierarchical Label Expansion Source: [Online Continual Learning on Hierarchical Label Expansion]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:25:24.889Z Authors: Byung Hyun Lee, Okchul Jung, Jonghyun Choi, Se Young Chun Published: 2023-08-28T07:42:26.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Online Continual Learning on CUB-200](): Online Continual Learning; CUB-200; AAUC: 52.2. - [Online Continual Learning on iNaturalist](): Online Continual Learning; iNaturalist; AAUC: 70.4. - [Online Continual Learning on FGVC-Aircraft](): Online Continual Learning; FGVC Aircraft; AAUC: 26.1. ## Original sources and implementation [Original paper]() [PDF]()