Generative Models in Deep Learning

Sergey I. Nikolenko ()
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Sergey I. Nikolenko: Synthesis AI

Chapter Chapter 4 in Synthetic Data for Deep Learning, 2021, pp 97-137 from Springer

Abstract: Abstract So far, we have mostly discussed discriminative machine learning models that aim to solve a supervised problem, i.e., learn a conditional distribution of the target variable conditioned on the input. In this chapter, we consider generative models whose purpose is to learn the entire distribution of inputs and be able to sample new inputs from this distribution. We will go through a general introduction to generative models and then proceed to generative models in deep learning. First, we will discuss explicit density models that model distribution factors with deep neural networks and their important special case, normalizing flows, and explicit density models that approximate the distribution in question, represented by variational autoencoders. Then we will proceed to the main content, generative adversarial networks, discuss various adversarial architectures and loss functions, and give a case study of style transfer with GANs that is directly relevant to synthetic-to-real transfer.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-75178-4_4

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DOI: 10.1007/978-3-030-75178-4_4

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