This paper reviews the current state of the art in artificial intelligence (AI) technologies and
applications in the context of the creative industries. A brief background of AI, and …

Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

Communication systems to date primarily aim at reliably communicating bit sequences.
Such an approach provides efficient engineering designs that are agnostic to the meanings …

Denoising diffusion probabilistic models are a promising new class of generative models
that mark a milestone in high-quality image generation. This paper showcases their ability to …

Learning based video compression attracts increasing attention in the past few years. The
previous hybrid coding approaches rely on pixel space operations to reduce spatial and …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

The previous deep video compression approaches only use the single scale motion
compensation strategy and rarely adopt the mode prediction technique from the traditional …

We review a class of methods that can be collected under the name nonlinear transform
coding (NTC), which over the past few years have become competitive with the best linear …

We consider decentralized machine learning over a network where the training data is
distributed across $ n $ agents, each of which can compute stochastic model updates on …

In this paper we present aa deep generative model for lossy video compression. We employ
a model that consists of a 3D autoencoder with a discrete latent space and an …