Visualizations for universal deep-feature representations: survey and taxonomy

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F24%3A10469887" target="_blank" >RIV/00216208:11320/24:10469887 - isvavai.cz</a>

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=tq1aSBp7US" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=tq1aSBp7US</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10115-023-01933-3" target="_blank" >10.1007/s10115-023-01933-3</a>

Result language

angličtina

Original language name

Visualizations for universal deep-feature representations: survey and taxonomy

Original language description

In data science and content-based retrieval, we find many domain-specific techniques that employ a data processing pipeline with two fundamental steps. First, data entities are represented by some visualizations, while in the second step, the visualizations are used with a machine learning model to extract deep features. Deep convolutional neural networks (DCNN) became the standard and reliable choice. The purpose of using DCNN is either a specific classification task or just a deep feature representation of visual data for additional processing (e.g., similarity search). Whereas the deep feature extraction is a domain-agnostic step in the pipeline (inference of an arbitrary visual input), the visualization design itself is domain-dependent and ad hoc for every use case. In this paper, we survey and analyze many instances of data visualizations used with deep learning models (mostly DCNN) for domain-specific tasks. Based on the analysis, we synthesize a taxonomy that provides a systematic overview of visualization techniques suitable for usage with the models. The aim of the taxonomy is to enable the future generalization of the visualization design process to become completely domain-agnostic, leading to the automation of the entire feature extraction pipeline. As the ultimate goal, such an automated pipeline could lead to universal deep feature data representations for content-based retrieval.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA22-21696S" target="_blank" >GA22-21696S: Deep Visual Representations of Unstructured Data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Knowledge and Information Systems

ISSN

0219-1377

e-ISSN

0219-3116

Volume of the periodical

66

Issue of the periodical within the volume

February

Country of publishing house

GB - UNITED KINGDOM

Number of pages

30

Pages from-to

811-840

UT code for WoS article

001066953600004

EID of the result in the Scopus database

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