Just Say the Name: Online Continual Learning with Category Names Only via Data Generation

arXiv:2403.10853v1 Announce Type: new
Abstract: 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 …

abstract annotation arts arxiv challenges continual costs cs.ai cs.cv cs.lg data prior restrictions scale supervised training training type usage via web world