Visual Analytics in Deep Learning

Fred Hohman, Minsuk Kahng, Robert Pienta, Duen Horng Chau

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.

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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.

Interpretability & Explainability
Debugging & Improving Models
Comparing & Selecting Models
Model Developers & Builders
Model Users
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

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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.

  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},