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 …

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

Collective insights from a group of experts have always proven to outperform an individual's
best diagnostic for clinical tasks. For the task of medical image segmentation, existing …

Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …

Recently the proliferation of highly realistic synthetic images facilitated through a variety of
GANs and Diffusions has significantly heightened the susceptibility to misuse. While the …

Generative adversarial networks (GANs) are challenging to train stably, and a promising
remedy of injecting instance noise into the discriminator input has not been very effective in …

The free access to large-scale public databases, together with the fast progress of deep
learning techniques, in particular Generative Adversarial Networks, have led to the …

This paper focuses on domain generalization (DG), the task of learning from multiple source
domains a model that generalizes well to unseen domains. A main challenge for DG is that …

Deep neural networks can generate images that are astonishingly realistic, so much so that
it is often hard for humans to distinguish them from actual photos. These achievements have …

Despite recent progress in generative image modeling, successfully generating high-
resolution, diverse samples from complex datasets such as ImageNet remains an elusive …