Technology of Graphic & Image
|
App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 4
1221-1227

TCSNGAN: image generation model based on Transformer and CNN with spectral normalization

Qian Huimin
Mao Qiuling
Chen Shi
Han Yixing
Lyu Benjie
College of Artificial Intelligence & Automation, Hohai University, Nanjing 211106, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0357

Abstract

GAN has become one of the commonly-used image generation models. However, the discriminator of GAN is prone to the vanishing gradient problem in the training process, which leads to the instability of training. So that it is difficult to obtain the optimal GAN, and the quality of generation image is poor. To solve this problem, it designed a CNN with spectral normalization which satisfied the Lipchitz condition as the discriminator. Together with the Transformer generator, this paper proposed an image generation model, namely TCSNGAN(Transformer CSN GAN). The network structure of discriminator was simple, which could solve the problem of training instability of GAN model, and could configure the number of adjustable CSN modules according to the image resolution of data sets to achieve the optimal performance of the model. Experiments on public datasets CIFAR-10 and STL-10 show that the proposed TCSNGAN model has low complexity, and the generated image quality is good. And the experiments of fire image generation task demonstrates the effectiveness of small-sample dataset augmentation.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0357
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 4
Section: Technology of Graphic & Image
Pages: 1221-1227
Serial Number: 1001-3695(2024)04-038-1221-07

Publish History

[2023-11-02] Accepted Paper
[2024-04-05] Printed Article

Cite This Article

钱惠敏, 毛邱凌, 陈实, 等. TCSNGAN:基于Transformer和谱归一化CNN的图像生成模型 [J]. 计算机应用研究, 2024, 41 (4): 1221-1227. (Qian Huimin, Mao Qiuling, Chen Shi, et al. TCSNGAN: image generation model based on Transformer and CNN with spectral normalization [J]. Application Research of Computers, 2024, 41 (4): 1221-1227. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)