Continual Learning with Evolving Class Ontologies

Lifelong learners must recognize concept vocabularies that evolve over time. A common yet underexplored scenario is learning with class labels that continually refine/expand old classes. For example, humans learn to recognize {\tt dog} before dog breeds. In practical settings, dataset {\it versioning} often introduces refinement to ontologies, such as autonomous vehicle benchmarks that refine a previous {\tt vehicle} class into {\tt school-bus} as autonomous operations expand to new cities. This paper formalizes a protocol for studying the problem of {\it Learning with Evolving Class Ontology} (LECO).