In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …

Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

Deep learning has demonstrated tremendous success in variety of application domains in
the past few years. This new field of machine learning has been growing rapidly and applied …

It is increasingly common in many types of natural and physical systems (especially
biological systems) to have different types of measurements performed on the same …

Score-based generative models (SGMs) are a recent breakthrough in generating fake
images. SGMs are known to surpass other generative models, eg, generative adversarial …

Deep learning approaches are playing an ever-increasing role throughout diagnostic
medicine, especially in neuroradiology, to solve a wide range of problems such as …

We propose a novel integral probability metric-based generative adversarial network (GAN),
called SphereGAN. In the proposed scheme, the distance between two probability …

The rapid development of artificial intelligence has enabled its use in many fields. However,
for complex design tasks, the question arises of how AI can effectively support humans and …

We propose a manifold matching approach to generative models which includes a
distribution generator (or data generator) and a metric generator. In our framework, we view …

We propose two new techniques for training Generative Adversarial Networks (GANs) in the
unsupervised setting. Our objectives are to alleviate mode collapse in GAN and improve the …