Shedding Light on Variational Autoencoders

被引：1

作者：

Ruiz Vargas, J. C. ^{[1
]}

Novaes, S. F. ^{[1
]}

Cobe, R. ^{[1
]}

Iope, R. ^{[1
]}

Stanzani, S. ^{[1
]}

Tomei, T. R. ^{[1
]}

机构：

[1] Sao Paulo State Univ Unesp, Ctr Sci Comp NCC, Sao Paulo, SP, Brazil

来源：

2018 XLIV LATIN AMERICAN COMPUTER CONFERENCE (CLEI 2018) | 2018年

基金：

巴西圣保罗研究基金会;

关键词：

Variational Autoencoders; Machine Learning; Tensorflow; Fresnel diffraction;

D O I：

10.1109/CLEI.2018.00043

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Deep neural networks provide the canvas to create models of millions of parameters to fit distributions involving an equally large number of random variables. The contribution of this study is twofold. First, we introduce a diffraction dataset containing computer-based simulations of a Young's interference experiment. Then, we demonstrate the adeptness of variational autoencoders to learn diffraction patterns and extract a latent feature that correlates with the physical wavelength.

引用

页码：294 / 298

页数：5

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