Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physics, prior information and additional domain knowledge. Simple classical models are useful but sensitive to inaccuracies and may lead to poor performance when real systems display complex or dynamic behavior. On the other hand, purely data-driven approaches that are model-agnostic are becoming increasingly popular as datasets become abundant and the power of modern deep learning pipelines increases. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance, especially for supervised problems. However, DNNs typically require massive amounts of data and immense computational resources, limiting their applicability for some signal processing scenarios. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches. Such model-based deep learning methods exploit both partial domain knowledge, via mathematical structures designed for specific problems, as well as learning from limited data. In this article we survey the leading approaches for studying and designing model-based deep learning systems. We divide hybrid model-based/data-driven systems into categories based on their inference mechanism. We provide a comprehensive review of the leading approaches for combining model-based algorithms with deep learning in a systematic manner, along with concrete guidelines and detailed signal processing oriented examples from recent literature. Our aim is to facilitate the design and study of future systems on the intersection of signal processing and machine learning that incorporate the advantages of both domains.
翻译:信号处理、通信和控制传统上依赖古典统计模型技术。这种基于模型的方法使用数学公式,代表基本的物理、先前的信息和更多的领域知识。简单古典模型有用,但对不准确的情况敏感,在实际系统显示复杂或动态行为时可能导致不良性能。另一方面,纯数据驱动的模型-不可知性方法随着数据集的丰富和现代深层次学习管道的力量增加而越来越受欢迎。深神经网络(DNNS)使用通用结构,这些结构能够从数据中学习操作,并展示出出色的性能,特别是针对受监督的问题。然而,DNNS通常需要大量的数据和巨大的计算资源,从而限制其对某些信号处理情景的适用性。我们感兴趣的是混合技术,将原则性数学模型和数据驱动系统结合起来,以获益于这两种方法的优势。这种基于模型的深层次学习方法利用部分域知识,通过为具体问题设计的数学结构,以及从有限的数据中学习。我们调查了研究和设计基于模型的深层次学习系统的主要方法。我们把基于模型/数据驱动系统的方法与基于系统化研究的系统化研究方法结合起来。我们把基于模型/数据分析方法的深层次研究方法的深层次研究方法与系统化方法同以学习方式结合起来。