文献类型：期刊文献

英文题名：Image Synthesis under Limited Data: A Survey and Taxonomy

作者：Yang, Mengping[1,2]; Wang, Zhe[1,2]

机构：[1] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

年份：2023

外文期刊名：arXiv

收录：EI(收录号：20230285686)

语种：英文

外文关键词：Data accuracy - Data consistency

摘要：Deep generative models, which target reproducing the given data distribution to produce novel samples, have made unprecedented advancements in recent years. Their technical breakthroughs have enabled unparalleled quality in the synthesis of visual content. However, one critical prerequisite for their tremendous success is the availability of a sufficient number of training samples, which requires massive computation resources. When trained on limited data, generative models tend to suffer from severe performance deterioration due to overfitting and memorization. Accordingly, researchers have devoted considerable attention to develop novel models that are capable of generating plausible and diverse images from limited training data recently. Despite numerous efforts to enhance training stability and synthesis quality in the limited data scenarios, there is a lack of a systematic survey that provides 1) a clear problem definition, critical challenges, and taxonomy of various tasks; 2) an in-depth analysis on the pros, cons, and remain limitations of existing literature; as well as 3) a thorough discussion on the potential applications and future directions in the field of image synthesis under limited data. In order to fill this gap and provide a informative introduction to researchers who are new to this topic, this survey offers a comprehensive review and a novel taxonomy on the development of image synthesis under limited data. In particular, it covers the problem definition, requirements, main solutions, popular benchmarks, and remain challenges in a comprehensive and all-around manner. We hope this survey can provide an informative overview and a valuable resource for researchers and practitioners, and promote further progress and innovation in this important topic. Apart from the relevant references, we aim to constantly maintain a timely up-to-date repository to track the latest advances in this topic at GitHub/awesome-few-shot-generation. ? 2023, CC BY-NC-SA.