A task-and-technique centered survey on visual analytics for deep learning model engineering

García, Rafael; Telea, Alexandru; Silva, Bruno Castro da; Tørresen, Jim; Comba, João Luiz Dihl

doi:10.1016/j.cag.2018.09.018

Cited by 47 publications

(44 citation statements)

References 73 publications

(188 reference statements)

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“…In addition, 3 out of the 7 survey papers made a general search on the web. Garcia et al 32 and Liu et al 28 found papers by checking the references of other publications, which makes it hard to assess the systematization of their process objectively. In addition, 3 out of these 7 survey papers searched for specific keywords, such as deep neural networks (DNNs), which also inspired our own methodology.…”

Section: General Overviewmentioning

confidence: 99%

“…Endert et al 27 Garcia et al 32 Hohman et al 20 Liu et al 28 Lu et al 35 Sacha et al 36 Only those surveys that contained an explicit methodology are included. 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 ML: machine learning.…”

Section: Search Targets Referencementioning

confidence: 99%

“…Samek et al 42 Zhang and Zhu 39 Choo and Liu 30 Garcia et al 32 Grün et al 40 Hohman et al 20 Seifert et al 37 Yu and Shi 38 More general categories are represented in blue, while more specific ones are shown in red. DL seems to be the most popular subtopic of the surveys, with many papers describing the explanation of neural networks (NNs).…”

Section: Dudley and Kristensson 31mentioning

confidence: 99%

“…Garcia et al 32 explain the classical ideas behind ML and DL from a mathematical perspective, including deep feedforward networks (DFNs), CNNs, and RNNs. Additionally, they present a VA workflow which shows the tasks of the multiple phases of DL networks, placing emphasis on the value of VA for DL networks.…”

mentioning

confidence: 99%

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A survey of surveys on the use of visualization for interpreting machine learning models

Chatzimparmpas

Martins

Jusufi

et al. 2020

Information Visualization

112

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Research in machine learning has become very popular in recent years, with many types of models proposed to comprehend and predict patterns and trends in data originating from different domains. As these models get more and more complex, it also becomes harder for users to assess and trust their results, since their internal operations are mostly hidden in black boxes. The interpretation of machine learning models is currently a hot topic in the information visualization community, with results showing that insights from machine learning models can lead to better predictions and improve the trustworthiness of the results. Due to this, multiple (and extensive) survey articles have been published recently trying to summarize the high number of original research papers published on the topic. But there is not always a clear definition of what these surveys cover, what is the overlap between them, which types of machine learning models they deal with, or what exactly is the scenario that the readers will find in each of them. In this article, we present a meta-analysis (i.e. a “survey of surveys”) of manually collected survey papers that refer to the visual interpretation of machine learning models, including the papers discussed in the selected surveys. The aim of our article is to serve both as a detailed summary and as a guide through this survey ecosystem by acquiring, cataloging, and presenting fundamental knowledge of the state of the art and research opportunities in the area. Our results confirm the increasing trend of interpreting machine learning with visualizations in the past years, and that visualization can assist in, for example, online training processes of deep learning models and enhancing trust into machine learning. However, the question of exactly how this assistance should take place is still considered as an open challenge of the visualization community.

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Section: General Overviewmentioning

confidence: 99%

Section: Search Targets Referencementioning

confidence: 99%

Section: Dudley and Kristensson 31mentioning

confidence: 99%

mentioning

confidence: 99%

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A survey of surveys on the use of visualization for interpreting machine learning models

Chatzimparmpas

Martins

Jusufi

et al. 2020

Information Visualization

112

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show abstract

“…Explanatory techniques in this class aim to shed light upon the roles of various layers, units, and connections during both training and inference, and therefore both understand how and what the network has learned, and how to improve this process. A recent survey of techniques in this class is given in [49].…”

Section: Architecture-centric Techniquesmentioning

confidence: 99%

Visual Analytics for Machine Learning: Computing and Leveraging Decision Boundary Maps

Rodrigues¹

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What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice

Ferreira

Monteiro

2020

Lecture Notes in Computer Science

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A task-and-technique centered survey on visual analytics for deep learning model engineering

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Cited by 47 publications

References 73 publications

A survey of surveys on the use of visualization for interpreting machine learning models

A survey of surveys on the use of visualization for interpreting machine learning models

Visual Analytics for Machine Learning: Computing and Leveraging Decision Boundary Maps

What Are People Doing About XAI User Experience? A Survey on AI Explainability Research and Practice

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