scholarly journals Recognizing Characters in Art History Using Deep Learning

2019 ◽  
Author(s):  
Prathmesh Madhu ◽  
Ronak Kosti ◽  
Lara Mührenberg ◽  
Peter Bell ◽  
Andreas Maier ◽  
...  
Keyword(s):  
Deep Learning ◽  
Art History

scholarly journals The Zombification of Art History

2019 ◽  
Vol 11 (2) ◽  
pp. 28-35
Author(s):  
Tsila Hassine ◽  
Ziv Neeman
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Art History ◽  
The Past ◽  
New Directions ◽  
Artistic Image

In the past few years deep-learning AI neural networks have achieved major milestones in artistic image analysis and generation, producing what some refer to as ‘art.’ We reflect critically on some of the artistic shortcomings of a few projects that occupied the spotlight in recent years. We introduce the term ‘Zombie Art’ to describe the generation of new images of dead masters, as well as ‘The AI Reproducibility Test.’ We designate the problems inherent in AI and in its application to art history. In conclusion, we propose new directions for both AI-generated art and art history, in the light of these new powerful AI technologies of artistic image analysis and generation.

‘Immersed in Art’: Engaged learning in art and design history

2021 ◽  
Vol 17 (2) ◽  
pp. 235-252
Author(s):  
Lisa Chandler ◽  
Alistair Ward ◽  
Lisa Ward
Keyword(s):  
Deep Learning ◽  
Active Learning ◽  
Art History ◽  
Learning Design ◽  
Multimodal Learning ◽  
Design History ◽  
Engaged Learning ◽  
Immersive Environments ◽  
Art And Design ◽  
Student Comprehension

Established approaches to art history pedagogy typically involve a primarily passive form of instruction incorporating the viewing of works projected on screens. While such approaches can convey valuable information, they can also contribute to student disengagement and do not necessarily support deep learning. This article examines three learning initiatives incorporating an immersive teaching space to determine how these forms of technology-enhanced active learning might enhance student comprehension and engagement. The article considers how learning design incorporating the affordances of such immersive environments can provide multimodal learning experiences that stimulate student imaginations and support learning and engagement in a manner that complements rather than replaces traditional modes of instruction.

scholarly journals A quantitative analysis of the taxonomy of artistic styles

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Viviane Clay* ◽  
Johannes Schrumpf* ◽  
Yannick Tessenow* ◽  
Helmut Leder ◽  
Ulrich Ansorge ◽  
...  
Keyword(s):  
Deep Learning ◽  
Quantitative Analysis ◽  
Reaction Time ◽  
Art History ◽  
State Of The Art ◽  
Future Research ◽  
Singleton Search ◽  
Artistic Styles ◽  
Psychological Experiments

Classifying artists and their work as distinct art styles has been an important task of scholars in the field of art history. Due to its subjectivity, scholars often contradict one another. Our project investigated differences in aesthetic qualities of seven art styles through quantitative means. This was achieved with state-of-the-art deep-learning paradigms to generate new images resembling the style of an artist or entire era. We conducted psychological experiments to measure the behavior of subjects when viewing these new art images. Two different experiments were used: In an eye-tracking study, subjects viewed art-style-specific generated images. Eye movements were recorded and then compared between art styles. In a visual singleton search study, subjects had to locate a style-outlier image among three images of an alternative style. Reaction time and accuracy were measured and analyzed. These experiments show that there are measurable differences in behavior when viewing images of varying art styles. From these differences, we constructed hierarchical clusterings relating art styles based on the different behaviors of subjects viewing the samples. Our study reveals a novel perspective on the classification of artworks into stylistic eras and motivates future research in the domain of empirical aesthetics through quantitative means.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Art History as Genetic Epistemology

1985 ◽  
Vol 30 (12) ◽  
pp. 962-964
Author(s):  
Pavel Machotka
Keyword(s):  
Art History ◽  
Genetic Epistemology

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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