@inproceedings{19f3ac59c0884c6d9ed1598665839f6e,
title = "Performing Effective Generative Learning from a Single Image Only",
abstract = "Generative adversarial networks (GANs) can be well used for image generation. Yet their training typically requires large amounts of data, which may not be available. This paper proposes a new algorithm for effective generative learning given a single image only. The proposed method involves building GAN models with a hierarchical pyramid structure and a parallel-branch design that enables independent learning of the foreground and background areas. This work conducts a set of well-designed experiments. The results well demonstrate that the proposed method produces the images of higher quality and better diversity than existing methods do. Thus, this work advances the field of generative learning for image generation.",
keywords = "Image generation, few-shot learning, generative adversarial networks",
author = "Qihui Xu and Jinshu Chen and Jiacheng Tang and Qi Kang and Zhou, {Meng Chu}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 32nd Wireless and Optical Communications Conference, WOCC 2023 ; Conference date: 05-05-2023 Through 06-05-2023",
year = "2023",
doi = "10.1109/WOCC58016.2023.10139746",
language = "English (US)",
series = "32nd Wireless and Optical Communications Conference, WOCC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "32nd Wireless and Optical Communications Conference, WOCC 2023",
address = "United States",
}