# The Cognition of Audience to Artistic Style Transfer
## 1 Introduction
### Application of NST into Art  
Neural Style Transfer (NST) is a type of algorithm that uses deep neural networks to render a content image in different styles. In the early research stage of Non-Photorealistic Rendering (NPR), many stylization algorithms were designed to automatically turn photos into synthetic networks by stimulating artists to create art [3; 4]. However, these patch-based methods' limitation is that only low-level image features are adopted while often failing to capture image structures effectively [5]. [PERSON] et al. [6] are the first to apply Convolutional Neural Networks (CNNs) to transfer famous painting styles into photography. Since the algorithm of [PERSON] et al. does not have any explicit restrictions on the type of style images and needs ground truth results for training, it breaks the constraints of previous approaches. In this case, it ushered in the new field called Neural Style Transfer (NST). The method put forward by [PERSON] et al. based on the Gram-matrix-matching-based style representations requires a slow iterative optimization process, which is computationally expensive. Still, it is usually regarded as the gold-standard method in the community of NST [5; 7]. During the follow-up years, there are a series of optimization methods for NST that can fall into paired and non-paired methods. To be specific, the former is to transfer the style of one image to another, while the latter is to learn the style of multiple images in a dataset and then transfer it. As the experiment in this study tends to provide each painting and its corresponding conversion as a paired stimulus, it is more suitable to choose a pairing method. In addition to [PERSON] original algorithm, this type of methods consists of several representative algorithms, such as AdaIN [7] and WCT [8] for improving speedy, as well as MST [9] and SEMST [10] which focuses on the matching of semantic patterns between content images and style images. Inspired by the instance normalization, [PERSON] and [PERSON] [7] proposed a new interpretation by normalizing feature statistics to adaptive instance normalization (AdaIN), which first enables arbitrary style transfer in real-time. Like [PERSON] and [PERSON]'s method but replacing the AdaIN layer between the encoder and the decoder, [PERSON] et al. [8] embedded a pair of feature transforms, whitening, and coloring (WCT) into an image reconstruction network. However, these algorithms based on feed-forward networks failed to consider the feature details and local structure and  
Figure 1: The procedures for this study. suffered from wash-out artifacts [5; 9]. To match the content structure, [PERSON] et al. [9] first introduced a multimodal style transfer method called MST. He considered the semantic content structure and the matching with style patterns by using K-means to split style patterns from style image and combine it with content images via graph-cut. Recently, [PERSON] [10] analyzed the shortcomings of MST, such as the inability to consider the structural information of the content image or the loss of the style characteristics due to the feature's high dimensional and low-resolution characteristics space and the characteristics of the graph cut method. In further research, he proposed a structure-emphasized multimodal style transfer (SEMST) that can flexibly match the content cluster and the style cluster even if the number of clusters differs. Although there are many other papers and studies in the NST field, the purpose of this study is to explore the differences in audience cognition to the results of artistic style transfer, rather than review all methods. The way to compare different NST algorithms includes both qualitative and quantitative methods. The main quantitative evaluation metrics focus on transfer speed and loss comparison [5; 9]. Regarding the qualitative evaluation, the observation of style features and user study by voting are usually used. In the experiment of [PERSON] et al.