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 …

Talking human face generation aims at synthesizing a natural human face that talks in
correspondence to the given text or audio series. Implementing the recently developed …

Generative adversarial networks (GANs) have been extensively studied in recent years and
have been used to address several problems in the fields of image generation and computer …

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial
resolutions from a remotely sensed image with low spatial resolution. Recently, deep …

A significant robustness gap exists between machine intelligence and human perception
despite recent advances in deep learning. Deep learning is not provably secure. A critical …

Abstract Many existing conditional Generative Adversarial Networks (cGANs) are limited to
conditioning on pre-defined and fixed class-level semantic labels or attributes. We propose …

We introduce the concept of an-semimetric that satisfies the same axioms as a topological
metric, except for an arbitrarily small allowance to violate the triangle inequality. Under this …

Abstract The capability of Convolutional Neural Networks (CNNs) for sparse representation
has significant application to complex tasks like Representation Learning (RL). However …

With the proposed of Generative Adversarial Networks (GANs), the generative adversarial
models have been extensively studied in recent years. Although probability-based methods …

Existing Sequence-to-Sequence (Seq2Seq) Neural Machine Translation (NMT) shows
strong capability with High-Resource Languages (HRLs). However, this approach poses …