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Volume 38 Issue 3
May  2023
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Hua Q, Hu HW, Qian SY et al. Bi-GAE: A bidirectional generative auto-encoder. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38(3): 626−643 May 2023. DOI: 10.1007/s11390-023-1902-1.
Citation: Hua Q, Hu HW, Qian SY et al. Bi-GAE: A bidirectional generative auto-encoder. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 38(3): 626−643 May 2023. DOI: 10.1007/s11390-023-1902-1.
shu

Bi-GAE: A Bidirectional Generative Auto-Encoder

  • 1.

    Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

  • 2.

    Alibaba Group, Shanghai 201210, China

Funds: This work was supported by the Program of Technology Innovation of the Science and Technology Commission of Shanghai Municipality under Grant No. 21511104700, the Artificial Intelligence Technology Support Project of the Science and Technology Commission of Shanghai Municipality under Grant No. 22DZ1100103, and the Shanghai Informatization Development Special Project under Grant No. 202001030.
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  • Author Bio:

    Qin Hua is currently a Ph.D. candidate in computer science and technology of Shanghai Jiao Tong University (SJTU), Shanghai. His research interests include intelligent resource scheduling and scaling of micro services, deep learning, and time series forecasting

    Han-Wen Hu is currently a Ph.D. candidate in computer science and technology of Shanghai Jiao Tong University (SJTU), Shanghai. His research interests include intelligent transportation, map matching algorithm, and time series forecasting

    Shi-You Qian received his Ph.D. degree in computer science and technology from Shanghai Jiao Tong University (SJTU), Shanghai, in 2015. He is currently an associate researcher with the Department of Computer Science and Engineering, SJTU, Shanghai. His research interests include event matching for content-based publish/subscribe systems, resource scheduling for the hybrid cloud, and driving recommendations with vehicular networks

    Ding-Yu Yang received his Ph.D. degree in computer science and technology from Shanghai Jiao Tong University, Shanghai, in 2015. He is currently a senior engineer at Alibaba Group, Shanghai. His research interests include resource prediction, anomaly detection in cloud computing, and distributed stream processing. He has published over 20 papers in some journals and conferences such as SIGMOD, VLDB, and VLDBJ

    Jian Cao received his Ph.D. degree in computer science and technology from the Nanjing University of Science and Technology, Nanjing, in 2000. He is currently a professor with the Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai. His main research interests include service computing, network computing, and intelligent data analytics

  • Corresponding author:

    qshiyou@sjtu.edu.cn

    This paper is the result of collaboration between Shanghai Jiao Tong University and Alibaba Group. Shi-You Qian is an associate researcher in Shanghai Jiao Tong University and Ding-Yu Yang is a senior engineer at Alibaba Group. Both of them can always assume the responsibilities and obligations of the corresponding author.

  • Received Date: September 06, 2021
  • Accepted Date: April 27, 2023
    Abstract

  • Improving the generative and representational capabilities of auto-encoders is a hot research topic. However, it is a challenge to jointly and simultaneously optimize the bidirectional mapping between the encoder and the decoder/generator while ensuing convergence. Most existing auto-encoders cannot automatically trade off bidirectional mapping. In this work, we propose Bi-GAE, an unsupervised bidirectional generative auto-encoder based on bidirectional generative adversarial network (BiGAN). First, we introduce two terms that enhance information expansion in decoding to follow human visual models and to improve semantic-relevant feature representation capability in encoding. Furthermore, we embed a generative adversarial network (GAN) to improve representation while ensuring convergence. The experimental results show that Bi-GAE achieves competitive results in both generation and representation with stable convergence. Compared with its counterparts, the representational power of Bi-GAE improves the classification accuracy of high-resolution images by about 8.09%. In addition, Bi-GAE increases structural similarity index measure (SSIM) by 0.045, and decreases Fréchet inception distance (FID) by 2.48 in the reconstruction of 512×512 images.

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