The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …

Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …

We present an approach to enhancing the realism of synthetic images. The images are
enhanced by a convolutional network that leverages intermediate representations produced …

Internet of Medical Things (IoMT) can connect many medical imaging equipment to the
medical information network to facilitate the process of diagnosing and treating doctors. As …

We present a novel neural-networks-based algorithm to compute optimal transport maps
and plans for strong and weak transport costs. To justify the usage of neural networks, we …

The appearance of generative adversarial networks (GAN) provides a new approach and
framework for computer vision. Compared with traditional machine learning algorithms, GAN …

Deep learning models have been used in several domains, however, adjusting is still
required to be applied in sensitive areas such as medical imaging. As the use of technology …

In the past few years, the Generative Adversarial Network (GAN), which proposed in 2014,
has achieved great success. There have been increasing research achievements based on …

CoMoGAN is a continuous GAN relying on the unsupervised reorganization of the target
data on a functional manifold. To that matter, we introduce a new Functional Instance …

Computer vision is one of the hottest research fields in deep learning. The emergence of
generative adversarial networks (GANs) provides a new method and model for computer …