Check out our Medium article on Multiple Views for a summary of the paper and takeaways from our survey!

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.

Overview of representative works in visual analytics for deep learning. Each row is one work; works are sorted alphabetically by first author’s lastname. Each column corresponds to a subsection from the six interrogative questions. A work’s relevant subsection is indicated by a colored cell.

 WHY WHO WHAT HOW WHEN WHERE Author Year Interpretability & Explainability Debugging & Improving Models Comparing & Selecting Models Education Model Developers & Builders Model Users Non-experts Computational Graph & Network Architecture Learned Model Parameters Individual Computational Units Neurons in High-dimensional Space Aggregated Information Node-link Diagrams for Network Architecture Dimensionality Reduction & Scatter Plots Line Charts for Temporal Metrics Instance-based Analysis & Exploration Interactive Experimentation Algorithms for Attribution & Feature Visualization During Training After Training Publication Venue Abadi, et al. 2016 arXiv Bau, et al. 2017 CVPR Bilal, et al. 2017 TVCG Bojarski, et al. 2016 arXiv Bruckner 2014 MS Thesis Carter, et al. 2016 Distill Cashman, et al. 2017 VADL Chae, et al. 2017 VADL Chung, et al. 2016 FILM Goyal, et al. 2016 arXiv Harley 2015 ISVC Hohman, et al. 2017 CHI Kahng, et al. 2018 TVCG Karpathy, et al. 2015 arXiv Li, et al. 2015 arXiv Liu, et al. 2017 TVCG Liu, et al. 2018 TVCG Ming, et al. 2017 VAST Norton & Qi 2017 VizSec Olah 2014 Web Olah, et al. 2018 Distill Pezzotti, et al. 2017 TVCG Rauber, et al. 2017 TVCG Robinson, et al. 2017 GeoHum. Rong, et al. 2016 ICML VIS Smilkov, et al. 2016 NIPS Workshop Smilkov, et al. 2017 ICML VIS Strobelt, et al. 2017 TVCG Tzeng & Ma 2005 VIS Wang, et al. 2018 TVCG Webster, et al. 2017 Web Wongsuphasawat, et al. 2018 TVCG Yosinski, et al. 2015 ICML DL Zahavy, et al. 2016 ICML Zeiler, et al. 2014 ECCV Zeng, et al. 2017 VADL Zhong, et al. 2017 ICML VIS Zhu, et al. 2016 ECCV Kahng, et al. 2018 TVCG Kwon, et al. 2018 TVCG Liu, et al. 2018 TVCG Liu, et al. 2018 VAST Strobelt, et al. 2018 TVCG Wang, et al. 2018 TVCG Zhang, et al. 2018 TVCG

Note: Works published after our survey paper's publication date (June 2018) appear below the black horizontal line.

Fred Hohman PhD Student Georgia Tech
Minsuk Kahng PhD Student Georgia Tech
Robert Pienta Research Scientist Symantec
Duen Horng Chau Associate Professor Georgia Tech

Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Fred Hohman, Minsuk Kahng, Robert Pienta, Duen Horng Chau.
IEEE Transactions on Visualization and Computer Graphics (TVCG). 2018.

@article{hohman2018visual,
title={Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers},
author={Hohman, Fred and Kahng, Minsuk and Pienta, Robert and Chau, Duen Horng},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2018},
publisher={IEEE}
}