Abstract: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 studyi… Show more
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