Supervised deep learning has achieved remarkable success in various applications. Successful machine learning application however depends on the availability of sufficiently large amount of data. In the absence of data from the target domain, representative data collection from multiple sources is often needed. However, a model trained on existing multi-source data might generalize poorly on the unseen target domain. This problem is referred to as domain shift. In this paper, we explore the suitability of multi-source training data selection to tackle the domain shift challenge in the context of domain generalization. We also propose a microservice-oriented methodology for supporting this solution. We perform our experimental study on the use case of building energy consumption prediction. Experimental results suggest that minimal building description is capable of improving cross-building generalization performances when used to select energy consumption data.

中文翻译：

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基于微服务的框架，用于在跨建筑物的知识转移中探索数据选择

监督式深度学习在各种应用程序中均取得了显著成功。然而，成功的机器学习应用取决于足够大量数据的可用性。在没有来自目标域的数据的情况下，通常需要从多个来源收集有代表性的数据。但是，在现有的多源数据上训练的模型可能在看不见的目标域上推广不佳。此问题称为域移位。在本文中，我们探索了多源训练数据选择在域通用化背景下解决域转换挑战的适用性。我们还提出了一种面向微服务的方法，以支持该解决方案。我们对建筑能耗预测的用例进行实验研究。