Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …

Recently, generative adversarial networks (GANs) have progressed enormously, which
makes them able to learn complex data distributions in particular faces. More and more …

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 …

The Generative Models have gained considerable attention in unsupervised learning via a
new and practical framework called Generative Adversarial Networks (GAN) due to their …

Abstract Convolutional Neural Networks (CNNs) are generally prone to noise interruptions,
ie, small image noise can cause drastic changes in the output. To suppress the noise effect …

To minimize the effects of age variation in face recognition, previous work either extracts
identity-related discriminative features by minimizing the correlation between identity-and …

Image inpainting aims to complete the missing or corrupted regions of images with realistic
contents. The prevalent approaches adopt a hybrid objective of reconstruction and …

StyleGAN2 is a state-of-the-art network in generating realistic images. Besides, it was
explicitly trained to have disentangled directions in latent space, which allows efficient …

Super-resolution (SR) is an ill-posed inverse problem where the size of the set of feasible
solutions that are consistent with a given low-resolution image is very large. Many …

We address the problem of single photo age progression and regression—the prediction of
how a person might look in the future, or how they looked in the past. Most existing aging …