[1]ZHAI Yongjie,ZHANG Zhibai,WANG Yaru.An image recognition method of zero -shot learning based on an improved TransGAN[J].CAAI Transactions on Intelligent Systems,2023,18(2):352-359.[doi:10.11992/tis.202111002]
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An image recognition method of zero -shot learning based on an improved TransGAN

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 2 Page number: 352-359 Column: 学术论文—机器感知与模式识别 Public date: 2023-05-05
Title:
An image recognition method of zero -shot learning based on an improved TransGAN
Author(s):
ZHAI Yongjie;  ZHANG Zhibai;  WANG Yaru
Department of Automation, North China Electric Power University, Baoding 071003, China
Keywords:
zero -shot learning; generative adversarial network; TransGAN; deep learning; image recognition; image feature; convolutional layer; nonlinear activation function
CLC:
TP18
DOI:
10.11992/tis.202111002
Abstract:
Zero-shot learning algorithms aim to address the challenge of image recognition with limited or even missing samples. By transforming the problem into a supervised learning task through the use of generative models, the method generates images of missing classes. However, the quality of generated images can be inconsistent and is susceptible to pattern collapse, affecting image recognition accuracy. To address this issue, we propose an improved zero-shot learning image recognition method based on an improved TransGAN. The generator of TransGAN is linked to a convolutional layer for dimensionality reduction, leading to a more effective extraction of image features and improved stability. Moreover, the addition of a nonlinear activation function to the discriminator and simplifying its structure enhances its ability to guide the generator and reduces computational requirements. Experiment results on public datasets show that our proposed method increases image recognition accuracy by 29.02% compared to the baseline model and demonstrates improved generalization performance.
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