  In real-world scenarios, extensive manual annotation for continual learning
is impractical due to prohibitive costs. Although prior arts, influenced by
large-scale webly supervised training, suggest leveraging web-scraped data in
continual learning, this poses challenges such as data imbalance, usage
restrictions, and privacy concerns. Addressing the risks of continual webly
supervised training, we present an online continual learning framework -
Generative Name only Continual Learning (G-NoCL). The proposed G-NoCL uses a
set of generators G along with the learner. When encountering new concepts
(i.e., classes), G-NoCL employs the novel sample complexity-guided data
ensembling technique DIverSity and COmplexity enhancing ensemBlER (DISCOBER) to
optimally sample training data from generated data. Through extensive
experimentation, we demonstrate superior performance of DISCOBER in G-NoCL
online CL benchmarks, covering both In-Distribution (ID) and
Out-of-Distribution (OOD) generalization evaluations, compared to naive
generator-ensembling, web-supervised, and manually annotated data.
