2 citations · 2 across the 4 of their papers we have counts for
4 papers
Measuring Orthogonality in Representations of Generative Models
Robin C. Geyer, Alessandro Torcinovich, João B. Carvalho +2
In unsupervised representation learning, models aim to distill essential features from high-dimensional data into lower-dimensional learned representations, guided by inductive bia…
Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective
João B. S. Carvalho, Mengtao Zhang, Robin Geyer +2
Anomaly detection (AD) is the machine learning task of identifying highly discrepant abnormal samples by solely relying on the consistency of the normal training samples. Under the…
Improving Explainability of Disentangled Representations using Multipath-Attribution Mappings
Lukas Klein, João B. S. Carvalho, Mennatallah El-Assady +3
Explainable AI aims to render model behavior understandable by humans, which can be seen as an intermediate step in extracting causal relations from correlative patterns. Due to th…
Spatially Dependent U-Nets: Highly Accurate Architectures for Medical Imaging Segmentation
João B. S. Carvalho, João A. Santinha, Đorđe Miladinović +1
In clinical practice, regions of interest in medical imaging often need to be identified through a process of precise image segmentation. The quality of this image segmentation ste…