Abstract
The rapid progress of deep neural network architectures is allowing both to automate the production of artworks and to extend the domain of creative expression. As such, it is opening new ground for professional and amateur artists alike. A major asset of these new computer processes is their capacity to derive, from a training phase, a generative model from which new artefacts can be produced. This attribute allows for a wide range of novel applications. New music or paintings in the style of famous artists can be produced at the click of a button, or combined to form new artworks. New graphical compositions can be “hallucinated” by the deep algorithmic models to produce striking, unexpected, visual forms. By the same token, the dependence on pre-existing, protected, artworks lays the ground for potential zones of friction with the rights holders of the source data that helped shape the generative model. This articulation, between the popular creative movement initiated by the deep neural architectures and the pre-existing rights of the authors, leads to a confrontation between the present legal framework for the protection of artistic creations and the new modalities offered by these new technological objects. The present work will address the conditions of protection of creations generated by deep neural networks under the main copyright regimes.
Highlights
Algorithmic productions have been part of the artistic landscape for more than half a century: from avant-garde procedural musical creations to new forms of computer graphics languages, they have opened innovative arenas for artistic expression and often served as exploration grounds for introducing and testing new computational tools that have become mainstream
Other deep neural architectures based on autoregressive generative models, such as WaveNet (Oord et al, 2016), break new ground in sound and music production
In order to explore some of the legal issues posed by these new creative tools, we will consider two applications of deep learning in the arts, where the technique has led to original applications: style transfer of graphical artworks and the automated generation of musical compositions from a training corpus
Summary
Algorithmic productions have been part of the artistic landscape for more than half a century: from avant-garde procedural musical creations to new forms of computer graphics languages, they have opened innovative arenas for artistic expression and often served as exploration grounds for introducing and testing new computational tools that have become mainstream. For a general discussion on the field of computational creativity, the reader is referred to the foundational work of Boden (1999, 2010) as well as the more recent studies by Colton and Wiggins (2012) and Jordanous (2012) This drastic change prefigures nothing less than a revolution in the modalities of personal expression as deep-learning frameworks permeate the creative toolkit available to professionals and amateurs alike. Doing so will depend upon, firstly, the definition of modalities of identification, within the creative artifact of a trace, a tangible imprint, of the creators’ intent and of his/ her personality and, secondly, the determination of the presumed contributors to the creation This analysis will be complemented by a discussion of two forms of deep creations: style transfer and training data selection
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