One-shot skill assessment in high-stakes domains with limited data via meta learning

arXiv:2301.00812v5 Announce Type: replace-cross
Abstract: Deep Learning (DL) has achieved robust competency assessment in various high-stakes fields. However, the applicability of DL models is often hampered by their substantial data requirements and confinement to specific training domains. This prevents them from transitioning to new tasks where data is scarce. Therefore, domain adaptation emerges as a critical element for the practical implementation of DL in real-world scenarios. Herein, we introduce A-VBANet, a novel meta-learning model capable of delivering domain-agnostic skill assessment …

abstract arxiv assessment cs.cv cs.lg data deep learning domains eess.iv fields however meta requirements robust tasks them training type via