Over the past few years, adversarial training has become an extremely active research topic
and has been successfully applied to various Artificial Intelligence (AI) domains. As a …

In this paper, we propose an adversarial network implementation for speech emotion
conversion as a data augmentation method, validated by a multi-class speech affect …

Existing GAN-based text-to-image models treat images as 2D pixel arrays. In this paper we
approach the text-to-image task from a different perspective where a 2D image is …

In creativity support and computational co-creativity contexts, the task of discovering
appropriate prompts for use with text-to-image generative models remains difficult. In many …

Affective computing is an interdisciplinary field that studies computational methods that
relate to or influence emotion. These methods have been applied to interactive media …

Recently, text-to-image generative Artificial Intelligence (AI) models have demonstrated their
ability to generate high-quality art with text prompts. However, generative AI is still incapable …

Background Generative art, which includes the development of artworks using algorithms
and emergent behaviour, is receiving recognition for its tendency to evoke emotion. A vast …

We design a deep learning framework that generates landscape images that match an given
emotion. We are working on a more challenging approach to generate landscape scenes …

We present HyperCGAN: a conceptually simple and general approach for text-to-image
synthesis that uses hypernetworks to condition a GAN model on text. In our setting, the …

Techniques for content generation are provided. A plurality of discriminative terms is
determined based at least in part on a first plurality of documents that are related to a first …