264 citations · 937 across the 33 of their papers we have counts for
38 papers
Deciphering the Definition of Adversarial Robustness for post-hoc OOD Detectors
Peter Lorenz, Mario Fernandez, Jens Müller +1
Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in real-world scenarios. In recent years, many OOD detectors have been developed, a…
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images
Michael Götz, Christian Weber, Franciszek Binczyk +7
We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for a…
Application-driven Validation of Posteriors in Inverse Problems
Tim J. Adler, Jan-Hinrich Nölke, Annika Reinke +8
Current deep learning-based solutions for image analysis tasks are commonly incapable of handling problems to which multiple different plausible solutions exist. In response, poste…
A Review of Change of Variable Formulas for Generative Modeling
Ullrich Köthe
Change-of-variables (CoV) formulas allow to reduce complicated probability densities to simpler ones by a learned transformation with tractable Jacobian determinant. They are thus…
Lifting Architectural Constraints of Injective Flows
Peter Sorrenson, Felix Draxler, Armand Rousselot +3
Normalizing Flows explicitly maximize a full-dimensional likelihood on the training data. However, real data is typically only supported on a lower-dimensional manifold leading the…
Training Invertible Neural Networks as Autoencoders
The-Gia Leo Nguyen, Lynton Ardizzone, Ullrich Köthe
Autoencoders are able to learn useful data representations in an unsupervised matter and have been widely used in various machine learning and computer vision tasks. In this work,…