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astro-ph.IM2025
The Platonic Universe: Do Foundation Models See the Same Sky?
UniverseTBD, :, Kshitij Duraphe +3
We test the Platonic Representation Hypothesis (PRH) in astronomy by measuring representational convergence across a range of foundation models trained on different data types. Usi…
astro-ph.IM2020
AstroVaDEr: Astronomical Variational Deep Embedder for Unsupervised Morphological Classification of Galaxies and Synthetic Image Generation
Ashley Spindler, James E. Geach, Michael J. Smith
We present AstroVaDEr, a variational autoencoder designed to perform unsupervised clustering and synthetic image generation using astronomical imaging catalogues. The model is a co…
astro-ph.IM2019
Generative deep fields: arbitrarily sized, random synthetic astronomical images through deep learning
Michael J. Smith, James E. Geach
Generative Adversarial Networks (GANs) are a class of artificial neural network that can produce realistic, but artificial, images that resemble those in a training set. In typical…