no code implementations • 20 Jul 2022 • Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann
We show that non-uniform diffusion leads to multi-scale diffusion models which have similar structure to this of multi-scale normalizing flows.
1 code implementation • 26 Nov 2021 • Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann
Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling.
no code implementations • NeurIPS Workshop Deep_Invers 2021 • Jevgenija Rudzusika, Buda Bajic, Ozan Öktem, Carola-Bibiane Schönlieb, Christian Etmann
We propose invertible Learned Primal-Dual as a method for tomographic image reconstruction.
no code implementations • 4 Jun 2021 • Georgios Batzolis, Marcello Carioni, Christian Etmann, Soroosh Afyouni, Zoe Kourtzi, Carola Bibiane Schönlieb
We model the conditional distribution of the latent encodings by modeling the auto-regressive distributions with an efficient multi-scale normalizing flow, where each conditioning factor affects image synthesis at its respective resolution scale.
no code implementations • 2 Mar 2021 • Jan Stanczuk, Christian Etmann, Lisa Maria Kreusser, Carola-Bibiane Schönlieb
Wasserstein GANs are based on the idea of minimising the Wasserstein distance between a real and a generated distribution.
1 code implementation • 23 Feb 2021 • Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry
In this work, we demonstrate that group equivariant convolutional operations can naturally be incorporated into learned reconstruction methods for inverse problems that are motivated by the variational regularisation approach.
no code implementations • 12 Feb 2021 • Christina Runkel, Christian Etmann, Michael Möller, Carola-Bibiane Schönlieb
An increasing number of models require the control of the spectral norm of convolutional layers of a neural network.
no code implementations • 14 Aug 2020 • Michael Roberts, Derek Driggs, Matthew Thorpe, Julian Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Aviles-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, Effrossyni Gkrania-Klotsas, James H. F. Rudd, Evis Sala, Carola-Bibiane Schönlieb
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and computed tomography (CT) images.
no code implementations • 5 Jun 2020 • Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann, Robert I McLachlan, Brynjulf Owren, Carola-Bibiane Schönlieb, Ferdia Sherry
Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing tasks.
2 code implementations • 11 May 2020 • Christian Etmann, Rihuan Ke, Carola-Bibiane Schönlieb
U-Nets have been established as a standard architecture for image-to-image learning problems such as segmentation and inverse problems in imaging.
no code implementations • 10 Dec 2019 • Christian Etmann, Maximilian Schmidt, Jens Behrmann, Tobias Boskamp, Lena Hauberg-Lotte, Annette Peter, Rita Casadonte, Jörg Kriegsmann, Peter Maass
Neural networks have recently been established as a viable classification method for imaging mass spectrometry data for tumor typing.
no code implementations • 16 Jun 2019 • Christian Etmann
In recent years, an increasing number of neural network models have included derivatives with respect to inputs in their loss functions, resulting in so-called double backpropagation for first-order optimization.
1 code implementation • 10 May 2019 • Christian Etmann, Sebastian Lunz, Peter Maass, Carola-Bibiane Schönlieb
Recent studies on the adversarial vulnerability of neural networks have shown that models trained to be more robust to adversarial attacks exhibit more interpretable saliency maps than their non-robust counterparts.
no code implementations • 2 May 2017 • Jens Behrmann, Christian Etmann, Tobias Boskamp, Rita Casadonte, Jörg Kriegsmann, Peter Maass
Deep learning offers an approach to learn feature extraction and classification combined in a single model.