HCV: Hierarchy-Consistency Verification for Incremental Implicitly-Refined Classification
This addresses the challenge of building concept hierarchies in incremental learning for AI systems, though it is incremental as it enhances existing methods rather than introducing a new paradigm.
The paper tackles the problem of incremental learning where new concepts need to be associated with old ones in a hierarchical manner, specifically in the Incremental Implicitly-Refined Classification (IIRC) setting, and shows that their Hierarchy-Consistency Verification (HCV) module improves performance of existing continual learning methods by a large margin on three setups.
Human beings learn and accumulate hierarchical knowledge over their lifetime. This knowledge is associated with previous concepts for consolidation and hierarchical construction. However, current incremental learning methods lack the ability to build a concept hierarchy by associating new concepts to old ones. A more realistic setting tackling this problem is referred to as Incremental Implicitly-Refined Classification (IIRC), which simulates the recognition process from coarse-grained categories to fine-grained categories. To overcome forgetting in this benchmark, we propose Hierarchy-Consistency Verification (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 and inference. Experiments on three setups of varying difficulty demonstrate that our HCV module improves performance of existing continual learning methods under this IIRC setting by a large margin. Code is available in https://github.com/wangkai930418/HCV_IIRC.