An Enhanced Dual-Stream Architecturefor State-of-the-ArtArtist and Style Classification

Doron Nevo; Eli David; Nathan S. Netanyahu

doi:10.1007/978-3-032-04546-1_45

An Enhanced Dual-Stream Architecturefor State-of-the-ArtArtist and Style Classification

Doron Nevo, Eli David, Nathan S. Netanyahu

College of Law and Business

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present an enhanced deep learning model that advances the state of the art in both artist and artistic style classification. Architectural refinements and optimized training boost artist classification accuracy to 96.3%, reducing the previous error rate by 43% (from 6.5% to 3.7%). Additionally, our model achieves a new benchmark in style classification with 75.3% accuracy, a 17% error reduction over the previous 71.2%. Comprehensive evaluations on large-scale datasets confirm the model’s robustness across diverse visual characteristics and class granularity levels.

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning – ICANN 2025 - 34th International Conference on Artificial Neural Networks, 2025, Proceedings
Editors	Walter Senn, Marcello Sanguineti, Ausra Saudargiene, Igor V. Tetko, Alessandro E. P. Villa, Viktor Jirsa, Yoshua Bengio
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	555-568
Number of pages	14
ISBN (Print)	9783032045454
DOIs	https://doi.org/10.1007/978-3-032-04546-1_45
State	Published - 2026
Event	34th International Conference on Artificial Neural Networks, ICANN 2025 - Kaunas, Lithuania Duration: 9 Sep 2025 → 12 Sep 2025

Publication series

Name	Lecture Notes in Computer Science
Volume	16069 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	34th International Conference on Artificial Neural Networks, ICANN 2025
Country/Territory	Lithuania
City	Kaunas
Period	9/09/25 → 12/09/25

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Keywords

Artist classification
Convolutional neural networks
Deep learning
Digital art analysis
Style classification

Access to Document

10.1007/978-3-032-04546-1_45

Cite this

Nevo, D., David, E., & Netanyahu, N. S. (2026). An Enhanced Dual-Stream Architecturefor State-of-the-ArtArtist and Style Classification. In W. Senn, M. Sanguineti, A. Saudargiene, I. V. Tetko, A. E. P. Villa, V. Jirsa, & Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025 - 34th International Conference on Artificial Neural Networks, 2025, Proceedings (pp. 555-568). (Lecture Notes in Computer Science; Vol. 16069 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-032-04546-1_45

Nevo, Doron ; David, Eli ; Netanyahu, Nathan S. / An Enhanced Dual-Stream Architecturefor State-of-the-ArtArtist and Style Classification. Artificial Neural Networks and Machine Learning – ICANN 2025 - 34th International Conference on Artificial Neural Networks, 2025, Proceedings. editor / Walter Senn ; Marcello Sanguineti ; Ausra Saudargiene ; Igor V. Tetko ; Alessandro E. P. Villa ; Viktor Jirsa ; Yoshua Bengio. Springer Science and Business Media Deutschland GmbH, 2026. pp. 555-568 (Lecture Notes in Computer Science).

@inproceedings{0e83478f3aef46b2a1b1b7a81c6de06b,

title = "An Enhanced Dual-Stream Architecturefor State-of-the-ArtArtist and Style Classification",

abstract = "We present an enhanced deep learning model that advances the state of the art in both artist and artistic style classification. Architectural refinements and optimized training boost artist classification accuracy to 96.3\%, reducing the previous error rate by 43\% (from 6.5\% to 3.7\%). Additionally, our model achieves a new benchmark in style classification with 75.3\% accuracy, a 17\% error reduction over the previous 71.2\%. Comprehensive evaluations on large-scale datasets confirm the model{\textquoteright}s robustness across diverse visual characteristics and class granularity levels.",

keywords = "Artist classification, Convolutional neural networks, Deep learning, Digital art analysis, Style classification",

author = "Doron Nevo and Eli David and Netanyahu, \{Nathan S.\}",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.; 34th International Conference on Artificial Neural Networks, ICANN 2025 ; Conference date: 09-09-2025 Through 12-09-2025",

year = "2026",

doi = "10.1007/978-3-032-04546-1\_45",

language = "אנגלית",

isbn = "9783032045454",

series = "Lecture Notes in Computer Science",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "555--568",

editor = "Walter Senn and Marcello Sanguineti and Ausra Saudargiene and Tetko, \{Igor V.\} and Villa, \{Alessandro E. P.\} and Viktor Jirsa and Yoshua Bengio",

booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2025 - 34th International Conference on Artificial Neural Networks, 2025, Proceedings",

address = "גרמניה",

}

Nevo, D, David, E & Netanyahu, NS 2026, An Enhanced Dual-Stream Architecturefor State-of-the-ArtArtist and Style Classification. in W Senn, M Sanguineti, A Saudargiene, IV Tetko, AEP Villa, V Jirsa & Y Bengio (eds), Artificial Neural Networks and Machine Learning – ICANN 2025 - 34th International Conference on Artificial Neural Networks, 2025, Proceedings. Lecture Notes in Computer Science, vol. 16069 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 555-568, 34th International Conference on Artificial Neural Networks, ICANN 2025, Kaunas, Lithuania, 9/09/25. https://doi.org/10.1007/978-3-032-04546-1_45

An Enhanced Dual-Stream Architecturefor State-of-the-ArtArtist and Style Classification. / Nevo, Doron; David, Eli; Netanyahu, Nathan S.
Artificial Neural Networks and Machine Learning – ICANN 2025 - 34th International Conference on Artificial Neural Networks, 2025, Proceedings. ed. / Walter Senn; Marcello Sanguineti; Ausra Saudargiene; Igor V. Tetko; Alessandro E. P. Villa; Viktor Jirsa; Yoshua Bengio. Springer Science and Business Media Deutschland GmbH, 2026. p. 555-568 (Lecture Notes in Computer Science; Vol. 16069 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Enhanced Dual-Stream Architecturefor State-of-the-ArtArtist and Style Classification

AU - Nevo, Doron

AU - David, Eli

AU - Netanyahu, Nathan S.

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

PY - 2026

Y1 - 2026

N2 - We present an enhanced deep learning model that advances the state of the art in both artist and artistic style classification. Architectural refinements and optimized training boost artist classification accuracy to 96.3%, reducing the previous error rate by 43% (from 6.5% to 3.7%). Additionally, our model achieves a new benchmark in style classification with 75.3% accuracy, a 17% error reduction over the previous 71.2%. Comprehensive evaluations on large-scale datasets confirm the model’s robustness across diverse visual characteristics and class granularity levels.

AB - We present an enhanced deep learning model that advances the state of the art in both artist and artistic style classification. Architectural refinements and optimized training boost artist classification accuracy to 96.3%, reducing the previous error rate by 43% (from 6.5% to 3.7%). Additionally, our model achieves a new benchmark in style classification with 75.3% accuracy, a 17% error reduction over the previous 71.2%. Comprehensive evaluations on large-scale datasets confirm the model’s robustness across diverse visual characteristics and class granularity levels.

KW - Artist classification

KW - Convolutional neural networks

KW - Deep learning

KW - Digital art analysis

KW - Style classification

UR - https://www.scopus.com/pages/publications/105016547160

U2 - 10.1007/978-3-032-04546-1_45

DO - 10.1007/978-3-032-04546-1_45

M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???

AN - SCOPUS:105016547160

SN - 9783032045454

T3 - Lecture Notes in Computer Science

SP - 555

EP - 568

BT - Artificial Neural Networks and Machine Learning – ICANN 2025 - 34th International Conference on Artificial Neural Networks, 2025, Proceedings

A2 - Senn, Walter

A2 - Sanguineti, Marcello

A2 - Saudargiene, Ausra

A2 - Tetko, Igor V.

A2 - Villa, Alessandro E. P.

A2 - Jirsa, Viktor

A2 - Bengio, Yoshua

PB - Springer Science and Business Media Deutschland GmbH

T2 - 34th International Conference on Artificial Neural Networks, ICANN 2025

Y2 - 9 September 2025 through 12 September 2025

ER -

Nevo D, David E, Netanyahu NS. An Enhanced Dual-Stream Architecturefor State-of-the-ArtArtist and Style Classification. In Senn W, Sanguineti M, Saudargiene A, Tetko IV, Villa AEP, Jirsa V, Bengio Y, editors, Artificial Neural Networks and Machine Learning – ICANN 2025 - 34th International Conference on Artificial Neural Networks, 2025, Proceedings. Springer Science and Business Media Deutschland GmbH. 2026. p. 555-568. (Lecture Notes in Computer Science). doi: 10.1007/978-3-032-04546-1_45