139 citations · 219 across the 11 of their papers we have counts for
12 papers
Non-Uniform Diffusion Models
Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb +1
Diffusion models have emerged as one of the most promising frameworks for deep generative modeling. In this work, we explore the potential of non-uniform diffusion models. We show…
Conditional Image Generation with Score-Based Diffusion Models
Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb +1
Score-based diffusion models have emerged as one of the most promising frameworks for deep generative modelling. In this work we conduct a systematic comparison and theoretical ana…
CAFLOW: Conditional Autoregressive Flows
Georgios Batzolis, Marcello Carioni, Christian Etmann +3
We introduce CAFLOW, a new diverse image-to-image translation model that simultaneously leverages the power of auto-regressive modeling and the modeling efficiency of conditional n…
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance)
Jan Stanczuk, Christian Etmann, Lisa Maria Kreusser +1
Wasserstein GANs are based on the idea of minimising the Wasserstein distance between a real and a generated distribution. We provide an in-depth mathematical analysis of differenc…
Equivariant neural networks for inverse problems
Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann +3
In recent years the use of convolutional layers to encode an inductive bias (translational equivariance) in neural networks has proven to be a very fruitful idea. The successes of…
Depthwise Separable Convolutions Allow for Fast and Memory-Efficient Spectral Normalization
Christina Runkel, Christian Etmann, Michael Möller +1
An increasing number of models require the control of the spectral norm of convolutional layers of a neural network. While there is an abundance of methods for estimating and enfor…
iUNets: Fully invertible U-Nets with Learnable Up- and Downsampling
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. For large-scale data, as it for e…
Structure preserving deep learning
Elena Celledoni, Matthias J. Ehrhardt, Christian Etmann +4
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…
Deep Relevance Regularization: Interpretable and Robust Tumor Typing of Imaging Mass Spectrometry Data
Christian Etmann, Maximilian Schmidt, Jens Behrmann +6
Neural networks have recently been established as a viable classification method for imaging mass spectrometry data for tumor typing. For multi-laboratory scenarios however, certai…
A Closer Look at Double Backpropagation
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 backpropagati…
On the Connection Between Adversarial Robustness and Saliency Map Interpretability
Christian Etmann, Sebastian Lunz, Peter Maass +1
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…
Deep Learning for Tumor Classification in Imaging Mass Spectrometry
Jens Behrmann, Christian Etmann, Tobias Boskamp +3
Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, a…