1 citations · 1 across the 5 of their papers we have counts for
5 papers
Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation
Svetlana Pavlitska, Haixi Fan, Konstantin Ditschuneit +1
Sparse mixture-of-experts (MoE) layers have been shown to substantially increase model capacity without a proportional increase in computational cost and are widely used in transfo…
Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers
Svetlana Pavlitska, Haixi Fan, Konstantin Ditschuneit +1
Robustifying convolutional neural networks (CNNs) against adversarial attacks remains challenging and often requires resource-intensive countermeasures. We explore the use of spars…
Self-Distilled Representation Learning for Time Series
Felix Pieper, Konstantin Ditschuneit, Martin Genzel +2
Self-supervised learning for time-series data holds potential similar to that recently unleashed in Natural Language Processing and Computer Vision. While most existing works in th…
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
Julien Siems, Konstantin Ditschuneit, Winfried Ripken +4
Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non…
Auto-Compressing Subset Pruning for Semantic Image Segmentation
Konstantin Ditschuneit, Johannes S. Otterbach
State-of-the-art semantic segmentation models are characterized by high parameter counts and slow inference times, making them unsuitable for deployment in resource-constrained env…