On Visualizations in the Role of Universal Data Representation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10420896" target="_blank" >RIV/00216208:11320/20:10420896 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3372278.3390743" target="_blank" >https://doi.org/10.1145/3372278.3390743</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3372278.3390743" target="_blank" >10.1145/3372278.3390743</a>

Result language
angličtina
Original language name
On Visualizations in the Role of Universal Data Representation
Original language description
The deep learning revolution changed the world of machine learning and boosted the AI industry as such. In particular, the most effective models for image retrieval are based on deep convolutional neural networks (DCNN), outperforming the traditional "hand-engineered" models by far. However, this tremendous success was redeemed by a high cost in the form of an exhaustive gathering of labeled data, followed by designing and training the DCNN models. In this paper, we outline a vision of a framework for instant transfer learning, where a generic pre-trained DCNN model is used as a universal feature extraction method for visualized unstructured data in many (non-visual) domains. The deep feature descriptors are then usable in similarity search tasks (database queries, joins) and in other parts of the data processing pipeline. The envisioned framework should enable practitioners to instantly use DCNN-based data representations in their new domains without the need for the costly training step. Moreover, by use of the framework the information visualization community could acquire a versatile metric for measuring the quality of data visualizations, which is generally a difficult task.
Czech name
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Czech description
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Type
D - Article in proceedings
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/GA19-01641S" target="_blank" >GA19-01641S: Contextual Similarity Search in Open Data</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2020 on International Conference on Multimedia Retrieval
ISBN
978-1-4503-7087-5
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
362-367
Publisher name
ACM
Place of publication
New York, NY, USA
Event location
Dublin, Irsko
Event date
Oct 26, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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