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Fangshi Zhou Research Thesis Dissertation July 31__final format approved LW 8-1-2022.pdf (2.31 MB)
ETD Abstract Container
Abstract Header
Improvement and Implementation of Gumbel-Softmax VAE
Author Info
Fangshi, Zhou
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1659485365234826
Abstract Details
Year and Degree
2022, Master of Computer Science (M.C.S.), University of Dayton, Computer Science.
Abstract
Variational autoencoders (VAE) have recently become one of the most interesting developments in deep learning, as they take input data (e.g., images or text), learn its latent space, and then generate new similar and smooth data. The ability of discovering the latent space and creating new data makes VAEs powerful generative models, having applications in dimensionality reduction, data reconstruction, text automatic generation, art design, unsupervised clustering, semi-supervised classification, anomaly/outlier detection, and so on. Classic VAEs consider a Gaussian latent space, which does not allow for more complex representations. Gumbel-Softmax VAEs are an interesting extension, which provides practical solutions to implement the reparameterization trick for sampling a one-hot vector from a categorical distribution. During training, Gumbel-Softmax VAE needs to rely on softmax temperature tau, which guides the annealing process for categorical latent variables. Prior work simply decreases the temperature by a fixed factor and ignores the impact of the starting value and the active range of the temperature. We find that the temperature directly determines the performance of training. We present a novel parallel structure for VAEs, which combines two symmetric VAEs with different updating mechanics for the temperature and adjusts it at each training epoch based on the loss from these two VAEs. We show that our model offers a better performance than the original Gumbel-Softmax VAEs and can be used for data reconstruction, anomaly detection, and renovation of the imperfect with relatively lower distortion and noises.
Committee
Zhongmei Yao (Committee Chair)
Luan V Nguyen (Committee Member)
Xin Chen (Committee Member)
Pages
53 p.
Subject Headings
Computer Science
Keywords
Gumbel-Softmax VAE
;
Softmax temperature
;
schemed of annealing temperature
;
parallel structure
;
anomaly detection
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Citations
Fangshi, Z. (2022).
Improvement and Implementation of Gumbel-Softmax VAE
[Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1659485365234826
APA Style (7th edition)
Fangshi, Zhou.
Improvement and Implementation of Gumbel-Softmax VAE.
2022. University of Dayton, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1659485365234826.
MLA Style (8th edition)
Fangshi, Zhou. "Improvement and Implementation of Gumbel-Softmax VAE." Master's thesis, University of Dayton, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1659485365234826
Chicago Manual of Style (17th edition)
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Document number:
dayton1659485365234826
Download Count:
1,157
Copyright Info
© 2022, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.