2 citations · 2 across the 4 of their papers we have counts for
4 papers
Edge-Aligned Initialization of Kernels for Steered Mixture-of-Experts
Martin Determann, Elvira Fleig
Steered Mixture-of-Experts (SMoE) has recently emerged as a powerful framework for spatial-domain image modeling, enabling high-fidelity image representation using a remarkably sma…
Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection
Conrad Orglmeister, Erik Bochinski, Volker Eiselein +1
Understanding the decision-making and trusting the reliability of Deep Machine Learning Models is crucial for adopting such methods to safety-relevant applications. We extend self-…
Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising
Elvira Fleig, Erik Bochinski, Thomas Sikora
Research in the past years introduced Steered Mixture-of-Experts (SMoE) as a framework to form sparse, edge-aware models for 2D- and higher dimensional pixel data, applicable to co…
Edge-Aware Autoencoder Design for Real-Time Mixture-of-Experts Image Compression
Elvira Fleig, Jonas Geistert, Erik Bochinski +2
Steered-Mixtures-of-Experts (SMoE) models provide sparse, edge-aware representations, applicable to many use-cases in image processing. This includes denoising, super-resolution an…