5 papers
Data Compression with Stochastic Codes
Gergely Flamich, Deniz Gündüz
Machine learning has had a major impact on data compression over the last decade and opened up many new theoretical and applied fields of inquiry. This paper describes one such dir…
A Unified Framework for Diffusion Model Unlearning with f-Divergence
Nicola Novello, Federico Fontana, Luigi Cinque +2
Most existing methods for concept unlearning in text-to-image diffusion models minimize a mean squared error (MSE) loss between the denoiser outputs conditioned on a target and an…
Relay-Assisted Activation-Integrated SIM for Wireless Physical Neural Networks
Meng Hua, Deniz Gündüz
Wireless physical neural networks (WPNNs) have emerged as a promising paradigm for performing neural computation directly in the physical layer of wireless systems, offering low la…
Cache-enabled Generative Joint Source-Channel Coding for Evolving Semantic Communications
Shunpu Tang, Qianqian Yang, Jihong Park +3
Learning-based semantic communication (SemCom) has recently emerged as a promising paradigm for improving the transmission efficiency of wireless networks. However, existing method…
Extreme Video Compression with Pre-trained Diffusion Models
Bohan Li, Yiming Liu, Xueyan Niu +3
Diffusion models have achieved remarkable success in generating high quality image and video data. More recently, they have also been used for image compression with high perceptua…