论文标题
通过深度多任务指标回归不可靠的异质数据中亚组的相对细粒度变化
Regressing Relative Fine-Grained Change for Sub-Groups in Unreliable Heterogeneous Data Through Deep Multi-Task Metric Learning
论文作者
论文摘要
在人工智能的许多应用中，细粒度的变化检测和回归分析至关重要。实际上，由于缺乏可靠的基础真相信息和复杂性，因此这项任务通常是挑战的，这是由于影响系统的许多基本因素之间的相互作用而产生的。因此，开发一个可以代表多个信息源的相关性和可靠性至关重要的框架。在本文中，我们调查了如何将多任务指标学习中的技术应用于实际数据中的细粒度变化。关键思想是，如果我们将特定对象的特定实例之间的兴趣指标纳入了一个任务之一，将其作为一个多任务指标中的一个任务之一，那么将其解释为用户启动良好的态度，从而使对用户的态度更改为元素，从而使供应良好。研究的技术是专门针对处理异质数据源的专门量身定制的。每个任务的输入数据可能包含丢失的值，该值的比例和分辨率在任务之间不存在，并且数据包含非独立且相同分布的（非IID）实例。根据我们最初的实验实施结果的结果，并讨论了该域中的相关研究，这可能为进一步的研究提供了方向。
Fine-Grained Change Detection and Regression Analysis are essential in many applications of ArtificialIntelligence. In practice, this task is often challenging owing to the lack of reliable ground truth information andcomplexity arising from interactions between the many underlying factors affecting a system. Therefore,developing a framework which can represent the relatedness and reliability of multiple sources of informationbecomes critical. In this paper, we investigate how techniques in multi-task metric learning can be applied for theregression of fine-grained change in real data.The key idea is that if we incorporate the incremental change in a metric of interest between specific instancesof an individual object as one of the tasks in a multi-task metric learning framework, then interpreting thatdimension will allow the user to be alerted to fine-grained change invariant to what the overall metric isgeneralised to be. The techniques investigated are specifically tailored for handling heterogeneous data sources,i.e. the input data for each of the tasks might contain missing values, the scale and resolution of the values is notconsistent across tasks and the data contains non-independent and identically distributed (non-IID) instances. Wepresent the results of our initial experimental implementations of this idea and discuss related research in thisdomain which may offer direction for further research.
