GenOL: Generating Diverse Examples for Name-only Online Learning

📰 ArXiv cs.AI

GenOL generates diverse examples for name-only online learning to adapt to new concepts without manual annotation

advanced Published 1 Apr 2026

Action Steps

Identify the need for adapting to new concepts in online learning
Implement GenOL to generate diverse examples for name-only online learning
Evaluate the performance of GenOL in continual learning scenarios
Refine GenOL for improved results

Who Needs to Know This

ML researchers and engineers working on continual learning and online learning methods can benefit from GenOL to improve their models' adaptability to new concepts

Key Insight

💡 GenOL enables online learning methods to adapt to new concepts without requiring manual annotation

Key Takeaways

GenOL generates diverse examples for name-only online learning to adapt to new concepts without manual annotation

Full Article

Title: GenOL: Generating Diverse Examples for Name-only Online Learning

Abstract:
arXiv:2403.10853v4 Announce Type: replace-cross Abstract: Online learning methods often rely on supervised data. However, under data distribution shifts, such as in continual learning (CL), where continuously arriving online data streams incorporate new concepts (e.g., classes), real-time manual annotation is impractical due to its costs and latency, which hinder real-time adaptation. To alleviate this, 'name-only' setup has been proposed, requiring only the name of concepts, not the supervised

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