Machine learning has proved useful in many software disciplines, including computer vision, speech and audio processing, natural language processing, robotics and some other fields. However, its applicability has been significantly hampered due its black-box nature and significant resource consumption. Performance is achieved at the expense of enormous computational resource and usually compromising the robustness and trustworthiness of the model. Recent researches have been identifying a lack of interactivity as the prime source of these machine learning problems. Consequently, interactive machine learning (iML) has acquired increased attention of researchers on account of its human-in-the-loop modality and relatively efficient resource utilization. Thereby, a state-of-the-art review of interactive machine learning plays a vital role in easing the effort toward building human-centred models. In this paper, we provide a comprehensive analysis of the state-of-the-art of iML. We analyze salient research works using merit-oriented and application/task oriented mixed taxonomy. We use a bottom-up clustering approach to generate a taxonomy of iML research works. Research works on adversarial black-box attacks and corresponding iML based defense system, exploratory machine learning, resource constrained learning, and iML performance evaluation are analyzed under their corresponding theme in our merit-oriented taxonomy. We have further classified these research works into technical and sectoral categories. Finally, research opportunities that we believe are inspiring for future work in iML are discussed thoroughly.
翻译:事实证明,机器学习在许多软件学科,包括计算机视觉、语音和音频处理、自然语言处理、机器人和其他一些领域都证明是有用的,然而,由于它的黑盒性质和大量资源消耗,其适用性受到很大阻碍。业绩是以巨大的计算资源为代价实现的,通常会损害模型的稳健性和可信度。最近的研究发现,缺乏互动性是这些机器学习问题的主要根源。因此,互动式机器学习(iML)因其人际流动模式和相对高效的资源利用而引起研究人员的更多关注。因此,对交互式机器学习进行最先进的审查,在缓解建立以人为中心的模型的努力方面发挥着至关重要的作用。在本论文中,我们提供了对iML最新技术的全方位分析。我们利用自下而起的集群方法来生成iML研究工作的分类和相对有效的资源利用。我们关于对抗黑盒攻击和相应的iML防御系统的研究工作和相应的iML防御系统,探索性机能研究、资源限制和最终分析这些部门性研究机会。我们在以技术-ML中,我们正在深入地讨论其面向成果的研究,我们的投资研究的分类研究中,我们正在深入地分析。