no code implementations • 26 Sep 2023 • Georgia Kourmouli, Nikos Kostagiolas, Yannis Panagakis, Mihalis A. Nicolaou
We present a locality-aware method for interpreting the latent space of wavelet-based Generative Adversarial Networks (GANs), that can well capture the large spatial and spectral variability that is characteristic to satellite imagery.
2 code implementations • 3 Aug 2022 • Nikos Kostagiolas, Mihalis A. Nicolaou, Yannis Panagakis
In recent years, considerable advancements have been made in the area of Generative Adversarial Networks (GANs), particularly with the advent of style-based architectures that address many key shortcomings - both in terms of modeling capabilities and network interpretability.