27 citations · 89 across the 17 of their papers we have counts for
17 papers
The Robust Semantic Segmentation UNCV2023 Challenge Results
Xuanlong Yu, Yi Zuo, Zitao Wang +34
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segment…
Fixing Overconfidence in Dynamic Neural Networks
Lassi Meronen, Martin Trapp, Andrea Pilzer +2
Dynamic neural networks are a recent technique that promises a remedy for the increasing size of modern deep learning models by dynamically adapting their computational cost to the…
Transport with Support: Data-Conditional Diffusion Bridges
Ella Tamir, Martin Trapp, Arno Solin
The dynamic Schrödinger bridge problem provides an appealing setting for solving constrained time-series data generation tasks posed as optimal transport problems. It consists of l…
Anomaly Detection using Generative Models and Sum-Product Networks in Mammography Scans
Marc Dietrichstein, David Major, Martin Trapp +6
Unsupervised anomaly detection models which are trained solely by healthy data, have gained importance in the recent years, as the annotation of medical data is a tedious task. Aut…
Uncertainty-guided Source-free Domain Adaptation
Subhankar Roy, Martin Trapp, Andrea Pilzer +4
Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data a…
Disentangling Model Multiplicity in Deep Learning
Ari Heljakka, Martin Trapp, Juho Kannala +1
Model multiplicity is a well-known but poorly understood phenomenon that undermines the generalisation guarantees of machine learning models. It appears when two models with simila…