108 citations · 116 across the 2 of their papers we have counts for
5 papers
Privacy-preserving datasets by capturing feature distributions with Conditional VAEs
Francesco Di Salvo, David Tafler, Sebastian Doerrich +1
Large and well-annotated datasets are essential for advancing deep learning applications, however often costly or impossible to obtain by a single entity. In many areas, including…
Integrating kNN with Foundation Models for Adaptable and Privacy-Aware Image Classification
Sebastian Doerrich, Tobias Archut, Francesco Di Salvo +1
Traditional deep learning models implicity encode knowledge limiting their transparency and ability to adapt to data changes. Yet, this adaptability is vital for addressing user da…
unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features
Sebastian Doerrich, Francesco Di Salvo, Christian Ledig
We introduce unORANIC, an unsupervised approach that uses an adapted loss function to drive the orthogonalization of anatomy and image-characteristic features. The method is versat…
Unsupervised domain adaptation in brain lesion segmentation with adversarial networks
Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig +8
Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performan…
Is the deconvolution layer the same as a convolutional layer?
Wenzhe Shi, Jose Caballero, Lucas Theis +4
In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented. Firstly, What is the relationship between our propos…