1 code implementation • 15 Apr 2020 • Luigi T. Luppino, Mads A. Hansen, Michael Kampffmeyer, Filippo M. Bianchi, Gabriele Moser, Robert Jenssen, Stian N. Anfinsen
We propose to extract relational pixel information captured by domain-specific affinity matrices at the input and use this to enforce alignment of the code spaces and reduce the impact of change pixels on the learning objective.
no code implementations • 7 Sep 2019 • Luigi T. Luppino, Filippo M. Bianchi, Gabriele Moser, Stian N. Anfinsen
First, our method quantifies the similarity of affinity matrices computed from co-located image patches in the two images.
no code implementations • 13 Feb 2019 • Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi, Lorenzo Livi, Arnt-Børre Salberg, Robert Jenssen
A promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function.
no code implementations • 31 Jul 2018 • Luigi T. Luppino, Filippo M. Bianchi, Gabriele Moser, Stian N. Anfinsen
In this paper we propose a framework, based on image regression, to perform change detection in heterogeneous multitemporal satellite images, which has become a main topic in remote sensing.
no code implementations • 19 Jul 2018 • Michael Kampffmeyer, Sigurd Løkse, Filippo M. Bianchi, Robert Jenssen, Lorenzo Livi
Autoencoders learn data representations (codes) in such a way that the input is reproduced at the output of the network.