Conditional Generative Denoising Autoencoder

We present a generative denoising autoencoder model that has an embedded data classifier in its architecture in order to take advantage of class-based discriminating features and produce better data samples. The proposed model is a conditional generative model and is sampled with a Markov chain Mont...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 31; no. 10; pp. 4117 - 4129
Main Authors: Karatsiolis, Savvas, Schizas, Christos N.
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Data models
Decoding
image denoising
Image processing
image sampling
Learning
Markov chains
Markov processes
multilayer perceptron
Noise reduction
Supervised learning
Training
Unsupervised learning
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:We present a generative denoising autoencoder model that has an embedded data classifier in its architecture in order to take advantage of class-based discriminating features and produce better data samples. The proposed model is a conditional generative model and is sampled with a Markov chain Monte Carlo (MCMC) process according to a label that denotes the desired (or undesired) class or classes. In this sense, any chosen predefined class or characteristic may have a positive or negative effect on the image generation process, meaning that it can be instructed to be present or absent from the generated sample. We argue that allowing discriminative information in the form of feature detectors to be present in the latent representation of the autoencoder can be generally beneficial. This technique is an alternative approach to variational autoencoders (VAEs) that enforce a prior on the latent distribution. We further claim that supervised learning may be generally able to serve unsupervised learning through an interaction between the two paradigms. However, the extreme majority of research done on the interaction of the two learning regimes has the goal of using unsupervised learning to improve supervised learning. In this article, we explore the two learning paradigms' interaction in the opposite direction.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2019.2952203