创世深学习版权 (Copyright in Generative Deep Learning)

from arxiv, 16 pages. Second version contains updates after entry into force of EU's directive on copyright in the Digital Single Market, and corrections of typos. Third version contains a new section about GitHub Copilot and its copyright implications. Fourth version contains improvements in abstract, introduction and conclusions, and a general rearrangement of the central sections

Machine-generated artworks are now part of the contemporary art scene: they are attracting significant investments and they are presented in exhibitions together with those created by human artists. These artworks are mainly based on generative deep learning techniques, which have seen a formidable development and remarkable refinement in the very recent years. Given the inherent characteristics of these techniques, a series of novel legal problems arise. In this article, we consider a set of key questions in the area of generative deep learning for the arts, including the following: is it possible to use copyrighted works as training set for generative models? How do we legally store their copies in order to perform the training process? Who (if someone) will own the copyright on the generated data? We try to answer these questions considering the law in force in both the United States of America and the European Union, and potential future alternatives. We then extend our analysis to code generation, which is an emerging area of generative deep learning. Finally, we also formulate a set of practical guidelines for artists and developers working on deep learning generated art, as well as some policy suggestions for policymakers.

翻译：机器创造的艺术作品现已成为当代艺术场景的一部分:它们吸引了大量投资,并且与人类艺术家创作的艺术作品一起在展览中展示。这些艺术作品主要基于基因深层次的学习技术,这些技术在最近几年里取得了巨大的发展和显著的改进。鉴于这些技术的固有特点,产生了一系列新的法律问题。在本篇文章中,我们考虑了艺术基因深层次学习领域的一系列关键问题,包括:是否可以将版权作品用作基因化模型的培训?我们如何合法储存其复制品以开展培训过程?谁(如果有人)将拥有所产生数据的版权?我们试图回答这些问题,同时考虑到美利坚合众国和欧洲联盟的现行法律,以及未来可能的替代方法。我们然后将我们的分析扩大到代码生成,这是基因深层次学习的新兴领域。最后,我们还为从事深层次学习艺术的艺术家和开发者制定了一套实用指南,并为决策者制定了一些政策建议。