Image synthesis in visual machine learning - Zhan, Fangneng (Nanyang Technological University, 2021)
Image synthesis aims to generate realistic and high-fidelity images automatically. It has attracted increasing interests from both academia and industrial communities in recent years due to its wide applications in various artificial intelligence (AI) tasks as well as the recent advance of generative adversarial networks (GANs). Specifically, image synthesis could generate realistic images of different objects and scenes, hence forms one fundamental component in various design tasks for automated generation of artworks, fashion, advertisement posters, etc. In addition, image synthesis could generate self-annotated images which can be directly applied to deep model training, hence alleviate the data constraint as deep neural networks usually require large amounts of annotated training images that are expensive and time-consuming to collect manually. However, automated generation of realistic and self-annotated images is still facing two major challenges. First, the synthesis realism requires the presence of both image appearance (e.g. image colors, brightness, styles, etc.) and image geometry (e.g. object sizes, alignment, perspective, etc.). Second, generating self-annotated and useful training images is still an open research topic and most existing generation networks cannot handle it well due to the lack of diversity in their generated images.