Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Normalizing flows provide a general mechanism for defining expressive probability
distributions, only requiring the specification of a (usually simple) base distribution and a …

Generative models have been in existence for many decades. In the field of machine
learning, we come across many scenarios when directly learning a target is intractable …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

This report summarizes the tutorial presented by the author at NIPS 2016 on generative
adversarial networks (GANs). The tutorial describes:(1) Why generative modeling is a topic …

Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …

Unsupervised learning of probabilistic models is a central yet challenging problem in
machine learning. Specifically, designing models with tractable learning, sampling …

Kwang Gi Kim https://doi. org/10.4258/hir. 2016.22. 4.351 ing those who are beginning their
careers in deep learning and artificial intelligence research. The other target audience …

Generative adversarial networks are a kind of artificial intelligence algorithm designed to
solve the generative modeling problem. The goal of a generative model is to study a …