Self-Relevance Predicts the Aesthetic Appeal of Real and Synthetic Artworks Generated via Neural Style Transfer
研究发现艺术品的审美吸引力很大程度上取决于其与观者的自我相关性，通过神经风格迁移将个人照片转化为艺术品后，自我相关作品比对照图像更受喜爱，效果堪比人类创作。
What determines the aesthetic appeal of artworks? Recent work suggests that aesthetic appeal can, to some extent, be predicted from a visual artwork’s image features. Yet a large fraction of variance in aesthetic ratings remains unexplained and may relate to individual preferences. We hypothesized that an artwork’s aesthetic appeal depends strongly on self-relevance. In a first study ( N = 33 adults, online replication N = 208), rated aesthetic appeal for real artworks was positively predicted by rated self-relevance. In a second experiment ( N = 45 online), we created synthetic, self-relevant artworks using deep neural networks that transferred the style of existing artworks to photographs. Style transfer was applied to self-relevant photographs selected to reflect participant-specific attributes such as autobiographical memories. Self-relevant, synthetic artworks were rated as more aesthetically appealing than matched control images, at a level similar to human-made artworks. Thus, self-relevance is a key determinant of aesthetic appeal, independent of artistic skill and image features.