Generative Adversarial Network Algorithms in Art: Data Video

The recent development of machine learning to synthesize the dataset and manipulate images into new works of art, bringing essential changes in visual art and the method of art creation. The paper aims at applying the Generative Adversarial Network (GAN) to the new media art in particular the image generation and video synthesis through latent space interpolation, through the indirect training in GAN to process a series of still images as the dataset, the generated work presents the ability of machine algorithms in learning and processing the image creation, as well as the next stage of machine-made art. The generated images through latent space interpolation are the artificial imitation among the images by the machine, indicating a new form of image interpretation and representation where human’s intervention in art creation is restricted in the pre-data selection and post-data appreciation.