Search Results for author: Christian Etmann

Found 14 papers, 4 papers with code

Non-Uniform Diffusion Models

no code implementations20 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.


Conditional Image Generation with Score-Based Diffusion Models

1 code implementation26 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.

Conditional Image Generation

CAFLOW: Conditional Autoregressive Flows

no code implementations4 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.

Image-to-Image Translation Translation

Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)

no code implementations2 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.

Equivariant neural networks for inverse problems

1 code implementation23 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.

Inductive Bias

Structure preserving deep learning

no code implementations5 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.

iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling

2 code implementations11 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.

A Closer Look at Double Backpropagation

no code implementations16 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.

On the Connection Between Adversarial Robustness and Saliency Map Interpretability

1 code implementation10 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.

Adversarial Robustness

Cannot find the paper you are looking for? You can Submit a new open access paper.