Abstract Generative Adversarial Networks (GANs) can generate near photo realistic images
in narrow domains such as human faces. Yet, modeling complex distributions of datasets …

The creation or manipulation of facial appearance through deep generative approaches,
known as DeepFake, have achieved significant progress and promoted a wide range of …

In an effort to further advance semi-supervised generative and classification tasks, we
propose a simple yet effective training strategy called* dual pseudo training*(DPT), built …

Extensive empirical evidence demonstrates that conditional generative models are easier to
train and perform better than unconditional ones by exploiting the labels of data. So do score …

Normalizing Flows (NFs) are likelihood-based models for continuous inputs. They have
demonstrated promising results on both density estimation and generative modeling tasks …

Semi-supervised class-conditional image synthesis is typically performed by inferring and
injecting class labels into a conditional Generative Adversarial Network (GAN). The …

Generative image models have been extensively studied in recent years. In the
unconditional setting, they model the marginal distribution from unlabelled images. To allow …

Generative adversarial network has become the factual standard for high-quality image
synthesis. However, modeling the distribution of complex datasets (eg ImageNet and COCO …

We propose a novel GAN training scheme that can handle any level of labeling in a unified
manner. Our scheme introduces a form of artificial labeling that can incorporate manually …

Generative Adversarial Networks (GANs) have shown potential for generating images, but
have limitations when applied to complex datasets. To address these limitations, class …