Abstract Generative Adversarial Networks (GANs) are a type of deep neural network that
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …

Justifiably, while big data is the primary interest of research and public discourse, it is
essential to acknowledge that small data remains prevalent. The same technological and …

Several location-based services require accurate location information in indoor
environments. Recently, it has been shown that deep neural network (DNN) based received …

Accurate brain tumour segmentation is critical for tasks such as surgical planning, diagnosis,
and analysis, with magnetic resonance imaging (MRI) being the preferred modality due to its …

We present a new method to create spatial data using a generative adversarial network
(GAN). Our contribution uses coarse and widely available geospatial data to create maps of …

Abstract Generative Adversarial Network (GAN) is widely used in many generative
problems, including in spatial information sciences and urban systems. The data generated …

Generative adversarial networks have become a de facto approach to generate synthetic
data points that resemble their real counterparts. We tackle the situation where the realism of …

Rapid and accurate classification of medical images plays an important role in medical
diagnosis. Nowadays, for medical image classification, there are some methods based on …

Magnetic resonance imaging (MRI) presents a detailed image of the internal organs via a
magnetic field. Given MRI's non-invasive advantage in repeated imaging, the low-contrast …

Privacy-preserving image generation has been important for segments such as medical
domains that have sensitive and limited data. The benefits of guaranteed privacy come at …