System Development & Application
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 2
539-543

Zero-shot learning based on stacked semantic auto-encoder with low-rank embedding

Ran Ruisheng
Dong Shuhong
Li Jin
Wang Ning
College of Computer & Information Science, Chongqing Normal University, Chongqing 401331, China
DOI: 10.19734/j.issn.1001-3695.2022.06.0302

Abstract

In the field of image classification, existing methods such as deep learning require a large number of annotated samples for training and are unable to identify classes that do not appear in the training phase. Zero-shot learning tasks can effectively alleviate such problems. This study proposed a new zero-shot learning method, namely low-rank stacked semantic auto-encoder(LSSAE) based on stacked auto-encoder and low-rank embedding. The model was based on an encoding-decoding mechanism where the encoder learned a projection function with a low-rank structure for concatenating the visual feature space, the semantic space and the labels. It reconstructed the original visual features in the decoding stage. And the low-rank embedding enabled the learned model to share the semantic information of the seen classes when anticipating the unseen classes for better classification. Experiments were conducted on five common datasets in this study, and the results show that the proposed LSSAE outperforms existing zero-shot learning methods which is an effective zero-shot learning method.

Foundation Support

教育部人文社科规划项目(20YJAZH084)
重庆市技术创新与应用发展专项面上项目(cstc2020jscx-msxmX0190)
重庆市教委科学技术研究重点项目(KJZD-K202100505)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.06.0302
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 2
Section: System Development & Application
Pages: 539-543
Serial Number: 1001-3695(2023)02-037-0539-05

Publish History

[2022-08-29] Accepted Paper
[2023-02-05] Printed Article

Cite This Article

冉瑞生, 董殊宏, 李进, 等. 基于低秩堆栈式语义自编码器的零样本学习 [J]. 计算机应用研究, 2023, 40 (2): 539-543. (Ran Ruisheng, Dong Shuhong, Li Jin, et al. Zero-shot learning based on stacked semantic auto-encoder with low-rank embedding [J]. Application Research of Computers, 2023, 40 (2): 539-543. )

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.

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