204 citations · 609 across the 30 of their papers we have counts for
33 papers
Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging
Daniel Wolf, Tristan Payer, Catharina Silvia Lisson +4
Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning mod…
LLMMaps -- A Visual Metaphor for Stratified Evaluation of Large Language Models
Patrik Puchert, Poonam Poonam, Christian van Onzenoodt +1
Large Language Models (LLMs) have revolutionized natural language processing and demonstrated impressive capabilities in various tasks. Unfortunately, they are prone to hallucinati…
Weakly-Supervised Optical Flow Estimation for Time-of-Flight
Michael Schelling, Pedro Hermosilla, Timo Ropinski
Indirect Time-of-Flight (iToF) cameras are a widespread type of 3D sensor, which perform multiple captures to obtain depth values of the captured scene. While recent approaches to…
Neural Activation Patterns (NAPs): Visual Explainability of Learned Concepts
Alex Bäuerle, Daniel Jönsson, Timo Ropinski
A key to deciphering the inner workings of neural networks is understanding what a model has learned. Promising methods for discovering learned features are based on analyzing acti…
Monocular Depth Decomposition of Semi-Transparent Volume Renderings
Dominik Engel, Sebastian Hartwig, Timo Ropinski
Neural networks have shown great success in extracting geometric information from color images. Especially, monocular depth estimation networks are increasingly reliable in real-wo…
Contrastive Representation Learning for 3D Protein Structures
Pedro Hermosilla, Timo Ropinski
Learning from 3D protein structures has gained wide interest in protein modeling and structural bioinformatics. Unfortunately, the number of available structures is orders of magni…
Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization
Ngan Nguyen, Feng Liang, Dominik Engel +4
We propose a new microscopy simulation system that can depict atomistic models in a micrograph visual style, similar to results of physical electron microscopy imaging. This system…
Clean Implicit 3D Structure from Noisy 2D STEM Images
Hannah Kniesel, Timo Ropinski, Tim Bergner +5
Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3D sample on the scale of individual cell components. Unfortunately, these 2D images can be too noisy to b…
Gaussian Mixture Convolution Networks
Adam Celarek, Pedro Hermosilla, Bernhard Kerbl +2
This paper proposes a novel method for deep learning based on the analytical convolution of multidimensional Gaussian mixtures. In contrast to tensors, these do not suffer from the…
Visual Identification of Problematic Bias in Large Label Spaces
Alex Bäuerle, Aybuke Gul Turker, Ken Burke +4
While the need for well-trained, fair ML systems is increasing ever more, measuring fairness for modern models and datasets is becoming increasingly difficult as they grow at an un…
Variance-Aware Weight Initialization for Point Convolutional Neural Networks
Pedro Hermosilla, Michael Schelling, Tobias Ritschel +1
Appropriate weight initialization has been of key importance to successfully train neural networks. Recently, batch normalization has diminished the role of weight initialization b…
RADU: Ray-Aligned Depth Update Convolutions for ToF Data Denoising
Michael Schelling, Pedro Hermosilla, Timo Ropinski
Time-of-Flight (ToF) cameras are subject to high levels of noise and distortions due to Multi-Path-Interference (MPI). While recent research showed that 2D neural networks are able…
Data-driven deep density estimation
Patrik Puchert, Pedro Hermosilla, Tobias Ritschel +1
Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks a…
A3GC-IP: Attention-Oriented Adjacency Adaptive Recurrent Graph Convolutions for Human Pose Estimation from Sparse Inertial Measurements
Patrik Puchert, Timo Ropinski
Conventional methods for human pose estimation either require a high degree of instrumentation, by relying on many inertial measurement units (IMUs), or constraint the recording sp…
Finding Nano-Ötzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography
Ngan Nguyen, Ciril Bohak, Dominik Engel +6
Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with unprecedented potential for resolving submicron structural detail. Existing volume visualization methods, howe…
Blue Noise Plots
Christian van Onzenoodt, Gurprit Singh, Timo Ropinski +1
We propose Blue Noise Plots, two-dimensional dot plots that depict data points of univariate data sets. While often one-dimensional strip plots are used to depict such data, one of…
exploRNN: Understanding Recurrent Neural Networks through Visual Exploration
Alex Bäuerle, Patrick Albus, Raphael Störk +2
Due to the success of deep learning (DL) and its growing job market, students and researchers from many areas are interested in learning about DL technologies. Visualization has pr…
Deep Volumetric Ambient Occlusion
Dominik Engel, Timo Ropinski
We present a novel deep learning based technique for volumetric ambient occlusion in the context of direct volume rendering. Our proposed Deep Volumetric Ambient Occlusion (DVAO) a…
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures
Pedro Hermosilla, Marco Schäfer, Matěj Lang +6
Proteins perform a large variety of functions in living organisms, thus playing a key role in biology. As of now, available learning algorithms to process protein data do not consi…
An Environment for Sustainable Research Software in Germany and Beyond: Current State, Open Challenges, and Call for Action
Hartwig Anzt, Felix Bach, Stephan Druskat +40
Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes…
Enabling Viewpoint Learning through Dynamic Label Generation
Michael Schelling, Pedro Hermosilla, Pere-Pau Vazquez +1
Optimal viewpoint prediction is an essential task in many computer graphics applications. Unfortunately, common viewpoint qualities suffer from two major drawbacks: dependency on c…
Classifying the classifier: dissecting the weight space of neural networks
Gabriel Eilertsen, Daniel Jönsson, Timo Ropinski +2
This paper presents an empirical study on the weights of neural networks, where we interpret each model as a point in a high-dimensional space -- the neural weight space. To explor…
Multi-Resolution Rendering for Computationally Expensive Lighting Effects
Simon Besenthal, Sebastian Maisch, Timo Ropinski
Many lighting methods used in computer graphics such as indirect illumination can have very high computational costs and need to be approximated for real-time applications. These c…
Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning
Pedro Hermosilla, Tobias Ritschel, Timo Ropinski
We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of uns…