As an important data selection schema, active learning emerges as the essential component when iterating an Artificial Intelligence (AI) model. It becomes even more critical given the dominance of deep neural network based models, which are composed of a large number of parameters and data hungry, in application. Despite its indispensable role for developing AI models, research on active learning is not as intensive as other research directions. In this paper, we present a review of active learning through deep active learning approaches from the following perspectives: 1) technical advancements in active learning, 2) applications of active learning in computer vision, 3) industrial systems leveraging or with potential to leverage active learning for data iteration, 4) current limitations and future research directions. We expect this paper to clarify the significance of active learning in a modern AI model manufacturing process and to bring additional research attention to active learning. By addressing data automation challenges and coping with automated machine learning systems, active learning will facilitate democratization of AI technologies by boosting model production at scale.
翻译:作为一种重要的数据选择方法,积极学习在复制人工智能模型时成为重要组成部分,由于深层神经网络模型占主导地位,这些模型由大量参数和数据饥饿组成,因此更加重要。尽管开发人工智能模型具有不可或缺的作用,但积极学习的研究没有其他研究方向那样密集。在本文件中,我们从以下角度对通过深层积极学习方法的积极学习进行审查:1)积极学习的技术进步,2)在计算机视野中的积极学习的应用,3)利用或具有潜力利用积极学习促进数据重复的工业系统,4)目前的限制和未来研究方向。我们期望本文件澄清在现代人工智能模型制造过程中积极学习的重要性,并给积极学习带来更多的研究关注。通过应对数据自动化挑战和自动机器学习系统,积极学习将促进信息技术的民主化,促进规模模型生产。