How to Generate Synthetic Paintings to Improve Art Style Classification

Sarah Pires Pérez, Fabio Gagliardi Cozman

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


Indexing artwork is not only a tedious job; it is an impossible task to complete manually given the amount of online art. In any case, the automatic classification of art styles is also a challenge due to the relative lack of labeled data and the complexity of the subject matter. This complexity means that common data augmentation techniques may not generate useful data; in fact, they may degrade performance in practice. In this paper, we use Generative Adversarial Networks for data augmentation so as to improve the accuracy of an art style classifier, showing that we can improve performance of EfficientNet B0, a state of art classifier. To achieve this result, we introduce Class-by-Class Performance Analysis; we also present a modified version of the SAGAN training configuration that allows better control against mode collapse and vanishing gradient in the context of artwork.

Idioma originalInglés
Título de la publicación alojadaIntelligent Systems - 10th Brazilian Conference, BRACIS 2021, Proceedings, Part 2
EditoresAndré Britto, Karina Valdivia Delgado
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas16
ISBN (versión impresa)9783030916985
EstadoPublicada - 2021
Publicado de forma externa
Evento10th Brazilian Conference on Intelligent Systems, BRACIS 2021 - Virtual, Online
Duración: 29 nov. 20213 dic. 2021

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen13074 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


Conferencia10th Brazilian Conference on Intelligent Systems, BRACIS 2021
CiudadVirtual, Online


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