activity
20182025
most citedLossy Image Compression with Normalizing Flows

22 citations · 47 across the 5 of their papers we have counts for

collaborators

10 papers

cs.CV2025

Leveraging Diffusion Models for Stylization using Multiple Style Images

Dan Ruta, Abdelaziz Djelouah, Raphael Ortiz +1

Recent advances in latent diffusion models have enabled exciting progress in image style transfer. However, several key issues remain. For example, existing methods still struggle…

eess.IV2022★ 3 cited

Microdosing: Knowledge Distillation for GAN based Compression

Leonhard Helminger, Roberto Azevedo, Abdelaziz Djelouah +2

Recently, significant progress has been made in learned image and video compression. In particular the usage of Generative Adversarial Networks has lead to impressive results in th…

eess.IV2020★ 1 cited

Blind Image Restoration with Flow Based Priors

Leonhard Helminger, Michael Bernasconi, Abdelaziz Djelouah +2

Image restoration has seen great progress in the last years thanks to the advances in deep neural networks. Most of these existing techniques are trained using full supervision wit…

cs.CV2020★ 22 cited

Lossy Image Compression with Normalizing Flows

Leonhard Helminger, Abdelaziz Djelouah, Markus Gross +1

Deep learning based image compression has recently witnessed exciting progress and in some cases even managed to surpass transform coding based approaches that have been establishe…

cs.CV2019★ 21 cited

Content Adaptive Optimization for Neural Image Compression

Joaquim Campos, Simon Meierhans, Abdelaziz Djelouah +1

The field of neural image compression has witnessed exciting progress as recently proposed architectures already surpass the established transform coding based approaches. While, s…

stat.ML2018

Disentangled Dynamic Representations from Unordered Data

Leonhard Helminger, Abdelaziz Djelouah, Markus Gross +1

We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input. Our approach exploits regularities in sequential data t…