# Comparison of Cognitive Differences of Artworks between Artist and Artistic Style Transfer
### From Trying to Popularizing: The Implementation of AI in Painting and Its Value  
In 2005, futurist [PERSON] predicted the coming of the era of AI in his book 'The Singularity is Near' [5]. As AI technologies, such as AlphaGo, are widely used in many fields [6], research and applications on AI have begun to emerge. In recent years, the development of computer vision technology has provided opportunities to analyze paintings through high-level calculation methods [7; 8]. These calculation methods can increase the knowledge and abilities of artists, scholars, and curators [9]. Machine Learning (ML) is a technology that realizes AI and Deep Learning (DL), such as Artificial Neural Networks (ANNs), which are currently one of the most widely used types of machine learning. Deep learning methods include many hidden units of deep layers [10], which allow the learning of complex relationships in data [11].  
The first application of neural networks to the field of artistic generation is 'A Neural Algorithm of Artistic Style', based on a neural network proposed by [PERSON] et al. [12], which can realize the transfer of any style. Since then, there has been endless research on styles, conversion efficiency, application fields, and related technologies. According to training and feature extraction methods, this mode can be divided into \"Paired\" and \"Unpaired\". The so-called \"Paired\" method is a pre-trained model. The method involves using a style map and a content map to complete artistic style conversion after the computer's deep learning, such as the method of [PERSON] et al., AdaIN, WCT, etc. The \"Unpaired\" method involves extracting the common features of multiple images from a data set, and then using an algorithm to complete the purpose of style transfer, without any paired training stimuli, such as Pix2 Pix, CycleGAN, etc.  
In short, the learning and imitation effects of algorithms are the key to perceiving the value of art in current research, without considering the feelings and cognition of the general audience. Therefore, this research can be valuable as a bridge to span the gap between technology and human nature. Based on this, the head of the research team has also carried out a series of researches in recent years. For example, [PERSON], [PERSON] and [PERSON] [13] discussed how to use desktop virtual reality (Desktop VR), or head-mounted display virtual reality (HMD VR), to enhance the audience's experience when viewing artworks. In addition, [PERSON], [PERSON], [PERSON] and [PERSON] [14] proposed a framework for evaluating artistic style conversion from a cognitive perspective, based on the artist's coding attributes (Color, Stroke, Texture) and the audience's decoding awareness level (Technique, Semantics, Effect). It can be used to explore the audience's perception of the transfer of artistic styles.  
Actually, the AI algorithms are quite varied. Selecting and implementing a detailed AI algorithm to imitate artwork will have a significant impact on outcomes. There can be large differences in terms of quality between \"the paintings created by AI\", due to the different AI algorithms used. It should be noted that the focus of this research is whether the audience can distinguish paintings completed by AI or human beings, and what criteria their distinctions are based on. Therefore, this article does not involve a comparison between \"paintings generated by different AI algorithms\". However, this will be carried out in a follow-up study.