OpenHAIV: A Framework Towards Practical Open-World Learning
This addresses the challenge of practical open-world learning for AI systems, though it appears incremental as it combines existing techniques.
The paper tackles the problem of open-world learning by proposing OpenHAIV, a framework that integrates OOD detection, new class discovery, and incremental continual fine-tuning into a unified pipeline, enabling models to autonomously acquire and update knowledge in open-world environments.
Substantial progress has been made in various techniques for open-world recognition. Out-of-distribution (OOD) detection methods can effectively distinguish between known and unknown classes in the data, while incremental learning enables continuous model knowledge updates. However, in open-world scenarios, these approaches still face limitations. Relying solely on OOD detection does not facilitate knowledge updates in the model, and incremental fine-tuning typically requires supervised conditions, which significantly deviate from open-world settings. To address these challenges, this paper proposes OpenHAIV, a novel framework that integrates OOD detection, new class discovery, and incremental continual fine-tuning into a unified pipeline. This framework allows models to autonomously acquire and update knowledge in open-world environments. The proposed framework is available at https://haiv-lab.github.io/openhaiv .