论文标题
您的艺术有多深：一项关于单个任务，单模式神经网络中艺术理解极限的实验研究
How Deep is Your Art: An Experimental Study on the Limits of Artistic Understanding in a Single-Task, Single-Modality Neural Network
论文作者
论文摘要
艺术品含义的计算建模是复杂而困难的。这是因为艺术解释是多维和高度主观的。本文通过实验研究了一种流行的机器学习方法，可以将最先进的深卷卷积神经网络（DCNN）正确地将现代概念艺术作品区分为艺术策展人设计的画廊。提出了两个假设，指出DCNN模型使用属性进行分类，例如形状和颜色，但不是非证实的特性，例如历史上下文和艺术家意图。这两个假设使用旨在此目的的方法进行了实验验证。 VGG-11 DCNN在Imagenet数据集上进行了预先训练，并在由现实世界概念摄影画廊设计的手工制作的数据集上进行了判断性微调。实验结果支持这两个假设，表明DCNN模型忽略了非探测特性，并且仅使用属性进行艺术品分类。这项工作指出了当前的DCNN限制，该限制应通过未来的DNN模型来解决。
Computational modeling of artwork meaning is complex and difficult. This is because art interpretation is multidimensional and highly subjective. This paper experimentally investigated the degree to which a state-of-the-art Deep Convolutional Neural Network (DCNN), a popular Machine Learning approach, can correctly distinguish modern conceptual art work into the galleries devised by art curators. Two hypotheses were proposed to state that the DCNN model uses Exhibited Properties for classification, like shape and color, but not Non-Exhibited Properties, such as historical context and artist intention. The two hypotheses were experimentally validated using a methodology designed for this purpose. VGG-11 DCNN pre-trained on ImageNet dataset and discriminatively fine-tuned was trained on handcrafted datasets designed from real-world conceptual photography galleries. Experimental results supported the two hypotheses showing that the DCNN model ignores Non-Exhibited Properties and uses only Exhibited Properties for artwork classification. This work points to current DCNN limitations, which should be addressed by future DNN models.
