面向人工智能的生成增强数据集和注释框架(GADAFAI)

P. Corcoran, Hossein Javidnia, Joseph Lemley, Viktor Varkarakis
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引用次数: 0

摘要

人工智能(AI)的最新进展,特别是在计算视觉领域,是由大型公共数据集的可用性推动的。然而,随着人工智能开始进入嵌入式设备,将越来越需要工具来获取和重新获取来自特定传感系统的数据集,以训练新的设备模型。在本文中,介绍了数据采集框架的路线图,该框架可以构建从小种子数据集训练人工智能系统所需的大型合成数据集。证明这种框架的一个关键因素是对生成的数据集进行验证,并从生物特征(面部)数据集的初步工作中显示示例结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generative Augmented Dataset and Annotation Frameworks for Artificial Intelligence (GADAFAI)
Recent Advances in Artificial Intelligence (AI), particularly in the field of compute vision, have been driven by the availability of large public datasets. However, as AI begins to move into embedded devices there will be a growing need for tools to acquire and re-acquire datasets from specific sensing systems to train new device models. In this paper, a roadmap in introduced for a data-acquisition framework that can build the large synthetic datasets required to train AI systems from small seed datasets. A key element to justify such a framework is the validation of the generated dataset and example results are shown from preliminary work on biometric (facial) datasets.
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