Research on the Art of Image Generation in the Context of Machine Learning

In recent years, research in the cross-modal domain has been an active area of deep machine learning research. One of the hottest cross-modal subfields, text-to-image conversion, is to this day a challenging task that requires algorithms combining both natural language processing and computer vision modalities. Machine learning, as a cutting-edge computer technology and an emerging digital medium, provides a new mode of thinking for the creation of new media art. Media artists are able to further communicate and collaborate with computers, thus broadening creative ideas and expressions. The study of Artificial Intelligence art expression not only facilitates our thinking and understanding of today's science and technology, but also provides a new carrier and new revelation for the dissemination of contemporary art. The study of image generation art not only requires the model to be able to understand the information relationship between long and difficult texts, the decoder needs to decode correctly, understand the semantics of the text as well as the complex background information, but also requires the model to be able to be stably trained to generate a picture from scratch.