Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers

TL;DR

This survey reviews how visual analytics tools help interpret, debug, and improve deep learning models, emphasizing a human-centered approach to understanding their decision-making processes and identifying future research directions.

Contribution

It provides a comprehensive, human-centered summary of visual analytics applications in deep learning, highlighting its history, current state, and future challenges.

Findings

Visual analytics aids in understanding deep learning models.

Tools support debugging and model improvement.

The survey identifies open research problems.

Abstract

Deep learning has recently seen rapid development and received significant attention due to its state-of-the-art performance on previously-thought hard problems. However, because of the internal complexity and nonlinear structure of deep neural networks, the underlying decision making processes for why these models are achieving such 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, which highlights its short yet impactful history and thoroughly summarizes 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). We conclude by highlighting research directions and open research problems. This survey helps 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.