13 citations · 14 across the 6 of their papers we have counts for
6 papers
Neural Volumetric Blendshapes: Computationally Efficient Physics-Based Facial Blendshapes
Nicolas Wagner, Ulrich Schwanecke, Mario Botsch
Computationally weak systems and demanding graphical applications are still mostly dependent on linear blendshapes for facial animations. The accompanying artifacts such as self-in…
Federated Stain Normalization for Computational Pathology
Nicolas Wagner, Moritz Fuchs, Yuri Tolkach +1
Although deep federated learning has received much attention in recent years, progress has been made mainly in the context of natural images and barely for computational pathology.…
Scalable 3D Semantic Segmentation for Gun Detection in CT Scans
Marius Memmel, Christoph Reich, Nicolas Wagner +1
With the increased availability of 3D data, the need for solutions processing those also increased rapidly. However, adding dimension to already reliably accurate 2D approaches lea…
Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation
Sophie Burkhardt, Jannis Brugger, Nicolas Wagner +3
Most deep neural networks are considered to be black boxes, meaning their output is hard to interpret. In contrast, logical expressions are considered to be more comprehensible sin…
NeuralQAAD: An Efficient Differentiable Framework for High Resolution Point Cloud Compression
Nicolas Wagner, Ulrich Schwanecke
In this paper, we propose NeuralQAAD, a differentiable point cloud compression framework that is fast, robust to sampling, and applicable to high resolutions. Previous work that is…
Super-Selfish: Self-Supervised Learning on Images with PyTorch
Nicolas Wagner, Anirban Mukhopadhyay
Super-Selfish is an easy to use PyTorch framework for image-based self-supervised learning. Features can be learned with 13 algorithms that span from simple classification to more…