2 papers
cs.CV2026
Better Together: Evaluating the Complementarity of Earth Embedding Models
Thijs L van der Plas, Jacob JW Bakermans, Vishal Nedungadi +3
Earth embedding models transform Earth observation data into embeddings uniquely tied to locations on the Earth's surface. These models are typically evaluated in isolation, compar…
cs.LG2024
Prior Learning in Introspective VAEs
Ioannis Athanasiadis, Fredrik Lindsten, Michael Felsberg
Variational Autoencoders (VAEs) are a popular framework for unsupervised learning and data generation. A plethora of methods have been proposed focusing on improving VAEs, with the…