Style recommendation and simulation for handmade artworks using generative adversarial networks

Today, artificial intelligence (AI) is used in the design and production of artworks as a powerful tool that helps artists and designers to create more creative and attractive artworks. AI as a new tool has created new opportunities to create and improve artworks. Therefore, in this study, a new model for recommending and simulating the style of handmade artworks using generative adversarial networks (GANs) is presented. This model operates in two distinct phases: style recommendation and style simulation. The reason for using these two separate phases is to increase the accuracy and efficiency of the model. In the first phase, a pre-trained GAN is used to suggest different styles to the artist. In the second phase, a new GAN specifically designed to produce realistic images of handmade artwork is used to simulate the artist's selected style. Also, in order to improve the accuracy, the genetic algorithm (GA) was used to adjust the activity configuration of Self-Attention (SA) modules. The results of a case study on two types of data, including samples without and with the initial background pattern, GA-SAGAN model has been able to significantly reduce the validation error. As a result, adjusting the activity configuration of SA modules using GA can be effective in producing realistic and accurate artworks. The outputs produced by the GA-SAGAN model have a loss rate close to zero. Also, the Entropy values and Precision and Recall indices are higher than GAN and SAGAN models, it shows the higher diversity of production output patterns and the superiority of the proposed model.