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Human beings learn and accumulate hierarchical knowledge over their lifetime.This knowledge is associated with previous concepts for consolidation andhierarchical construction. However, current incremental learning methods lackthe ability to build a concept hierarchy by associating new concepts to oldones. A more realistic setting tackling this problem is referred to asIncremental Implicitly-Refined Classification (IIRC), which simulates therecognition process from coarse-grained categories to fine-grained categories.To overcome forgetting in this benchmark, we propose Hierarchy-ConsistencyVerification (HCV) as an enhancement to existing continual learning methods.Our method incrementally discovers the hierarchical relations between classes.We then show how this knowledge can be exploited during both training andinference. Experiments on three setups of varying difficulty demonstrate thatour HCV module improves performance of existing continual learning methodsunder this IIRC setting by a large margin. Code is available inhttps://github.com/wangkai930418/HCV_IIRC.

HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification