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Continual Learning (CL) often relies on the availability of extensiveannotated datasets, an assumption that is unrealistically time-consuming andcostly in practice. We explore a novel paradigm termed name-only continuallearning where time and cost constraints prohibit manual annotation. In thisscenario, learners adapt to new category shifts using only category nameswithout the luxury of annotated training data. Our proposed solution leveragesthe expansive and ever-evolving internet to query and download uncuratedwebly-supervised data for image classification. We investigate the reliabilityof our web data and find them comparable, and in some cases superior, tomanually annotated datasets. Additionally, we show that by harnessing the web,we can create support sets that surpass state-of-the-art name-onlyclassification that create support sets using generative models or imageretrieval from LAION-5B, achieving up to 25% boost in accuracy. When appliedacross varied continual learning contexts, our method consistently exhibits asmall performance gap in comparison to models trained on manually annotateddatasets. We present EvoTrends, a class-incremental dataset made from the webto capture real-world trends, created in just minutes. Overall, this paperunderscores the potential of using uncurated webly-supervised data to mitigatethe challenges associated with manual data labeling in continual learning.

From Categories to Classifiers: Name-Only Continual Learning by Exploring the Web