34 citations · 66 across the 15 of their papers we have counts for
6 papers · 1 filter
Leveraging Transfer Learning for Astronomical Image Analysis
Stefano Cavuoti, Lars Doorenbos, Demetra De Cicco +7
The exponential growth of astronomical data from large-scale surveys has created both opportunities and challenges for the astrophysics community. This paper explores the possibili…
Non-Linear Outlier Synthesis for Out-of-Distribution Detection
Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
The reliability of supervised classifiers is severely hampered by their limitations in dealing with unexpected inputs, leading to great interest in out-of-distribution (OOD) detect…
Learning Non-Linear Invariants for Unsupervised Out-of-Distribution Detection
Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
The inability of deep learning models to handle data drawn from unseen distributions has sparked much interest in unsupervised out-of-distribution (U-OOD) detection, as it is cruci…
Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models
Lars Doorenbos, Eva Sextl, Kevin Heng +6
Modern spectroscopic surveys can only target a small fraction of the vast amount of photometrically cataloged sources in wide-field surveys. Here, we report the development of a ge…
Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection
Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
Deep learning models are vulnerable to performance degradation when encountering out-of-distribution (OOD) images, potentially leading to misdiagnoses and compromised patient care.…
Identification of problematic epochs in astronomical time series through transfer learning
Stefano Cavuoti, Demetra De Cicco, Lars Doorenbos +4
We present a novel method for detecting outliers in astronomical time series based on the combination of a deep neural network and a k-nearest neighbor algorithm with the aim of id…