Computational intelligence, particularly deep learning, offers powerful tools for discriminating and generating samples such as images. Deep learning methods have been used in different artistic contexts for neural style transfer, artistic style recognition, and musical genre recognition. Using a constrained manifold analysis protocol, we discuss to what extent spaces induced by deep-learning convolutional neural networks can capture historical/stylistic progressions in music and visual art. We use a path-finding algorithm, called principal path, to move from one point to another. We apply it to the vector space induced by convolutional neural networks. We perform experiments with visual artworks and songs, considering a subset of classes. Within this simplified scenario, we recover a reasonable historical/stylistic progression in several cases. We use the principal path algorithm to conduct an evolutionary analysis of vector spaces induced by convolutional neural networks. We perform several experiments in the visual art and music spaces. The principal path algorithm finds reasonable connections between visual artworks and songs from different styles/genres with respect to the historical evolution when a subset of classes is considered. This approach could be used in many areas to extract evolutionary information from an arbitrary high-dimensional space and deliver interesting cognitive insights.

计算智能，尤其是深度学习，提供了强大的工具来区分和生成图像等样本。深度学习方法已在不同的艺术环境中用于神经风格转移，艺术风格识别和音乐流派识别。使用约束流形分析协议，我们讨论了深度学习卷积神经网络所诱导的空间可以在多大程度上捕获音乐和视觉艺术中的历史/风格发展。我们使用一种称为主路径的寻路算法来从一个点移动到另一点。我们将其应用于卷积神经网络诱导的向量空间。考虑类的子集，我们使用视觉艺术品和歌曲进行实验。在这种简化的情况下，我们在几种情况下恢复了合理的历史/风格发展。我们使用主路径算法对卷积神经网络引起的向量空间进行进化分析。我们在视觉艺术和音乐领域进行了一些实验。当考虑类别的子集时，主要路径算法可以找到视觉艺术品和不同风格/流派的歌曲之间的合理联系，以适应历史演变。这种方法可用于许多领域，以从任意高维空间中提取进化信息并提供有趣的认知见解。当考虑类别的子集时，主要路径算法可以找到视觉艺术品和不同风格/流派的歌曲之间的合理联系，以适应历史演变。这种方法可用于许多领域，以从任意高维空间中提取进化信息并提供有趣的认知见解。当考虑类别的子集时，主要路径算法可以找到视觉艺术品和不同风格/流派的歌曲之间的合理联系，以适应历史演变。这种方法可用于许多领域，以从任意高维空间中提取进化信息并提供有趣的认知见解。