Abstract Machine learning (ML), as the most promising paradigm to discover deep
knowledge from data, has been widely applied to practical applications, such as …

Much has been said about the fusion of bio-inspired optimization algorithms and Deep
Learning models for several purposes: from the discovery of network topologies and …

Medical image processing can lack images for diagnosis. Generative Adversarial Networks
(GANs) provide a method to train generative models for data augmentation. Synthesized …

As a subfield of deep learning (DL), generative adversarial networks (GANs) have produced
impressive generative results by applying deep generative models to create synthetic data …

Labeling images for classification can be expensive. Semi-Supervised Learning (SSL)
Generative Adversarial Network (GAN) methods train good classifiers with a few labeled …

Abstract Generative Adversarial Networks (GAN) is an adversarial model that became
relevant in the last years, displaying impressive results in generative tasks. A GAN combines …

In the last decade, generative models have seen widespread use for their ability to generate
diverse artefacts in an increasingly simple way. Historically, the use of evolutionary …

The predominant paradigm in evolutionary game theory and more generally online learning
in games is based on a clear distinction between a population of dynamic agents that …

The use of biomedical images for the training of various Deep Learning (DL) systems
oriented to health has reported a competitive performance. However, DL needs a large …

Generative adversarial networks (GANs) achieved relevant advances in the field of
generative algorithms, presenting high-quality results mainly in the context of images …