Continual Learning with Evolving Class Ontologies Zhiqiu Lin

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 dog before dog breeds. In practical settings, dataset versioning often introduces refinement to ontologies, such as autonomous vehicle benchmarks that refine a previous vehicle class into school-bus as autonomous operations expand to new cities. This paper formalizes a protocol for studying the problem of Learning with Evolving Class Ontology (LECO). LECO requires learning classifiers in distinct time periods (TPs); each TP introduces a new ontology of "fine" labels that refines old ontologies of "coarse" labels (e.g., dog breeds that refine the previous dog). LECO explores such questions as whether to annotate new data or relabel the old, how to exploit coarse labels, and whether to finetune the previous TP's model or train from scratch. To answer these questions, we leverage insights from related problems such as class-incremental learning.