Deep learning has recently seen rapid development and significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the innate complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such high performance are challenging and sometimes mystifying to interpret. As deep learning spreads across domains, it is of paramount importance that we equip users of deep learning with tools for understanding when a model works correctly, when it fails, and ultimately how to improve its performance. Standardized toolkits for building neural networks have helped democratize deep learning; visual analytics systems have now been developed to support model explanation, interpretation, debugging, and improvement. We present a survey of the role of visual analytics in deep learning research, noting its short yet impactful history and summarize the state-of-the-art using a human-centered interrogative framework, focusing on the Five W's and How (Why, Who, What, How, When, and Where), to thoroughly summarize deep learning visual analytics research. We conclude by highlighting research directions and open research problems. This survey helps new researchers and practitioners in both visual analytics and deep learning to quickly learn key aspects of this young and rapidly growing body of research, whose impact spans a diverse range of domains.

本文综述了可视化分析在深度学习中的角色，并且结合人类中心疑问框架，着重讨论了为什么、谁、什么、如何、何时和何地，从而全面总结了目前的研究现状，同时提出了未来研究的方向和问题。

深度学习中的可视化分析: 对下一步前沿的问询调查