Download This PaperDeep Variational Auto Encoder for Dimensionality Reduction, Denoising in MNIST Datasets Using TensorFlow and Keras
8 Pages Posted: 12 May 2020
Date Written: April 16, 2020
Abstract
Auto encoder is a data compression algorithm where the compression and decompression functions are data specific, lossy, learned automatically from examples rather than engineered from human. Variational Auto Encoders (VAE) is a type of auto encoder with added constrains on the encoded representations being learned. Clearly speaking it is an auto encoder that learns a latent variable model for its input data. The main objective of my paper is to provide a good visualization of data by denoising of an image using MNIST dataset and achieve better results.
Keywords: Denoising, MNIST, VAE, Tensor flow and Keras
Suggested Citation: Suggested Citation
Dar, Showkat Ahmad, Deep Variational Auto Encoder for Dimensionality Reduction, Denoising in MNIST Datasets Using TensorFlow and Keras (April 16, 2020). Available at SSRN: https://ssrn.com/abstract=3578118 or http://dx.doi.org/10.2139/ssrn.3578118
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