no code implementations • 7 Mar 2024 • Martin Willbo, Aleksis Pirinen, John Martinsson, Edvin Listo Zec, Olof Mogren, Mikael Nilsson
In this work we systematically examine model sensitivities with respect to several color- and texture-based distortions on the input EO data during inference, given models that have been trained without such distortions.
no code implementations • 22 Jun 2023 • Marcus Toftås, Emilie Klefbom, Edvin Listo Zec, Martin Willbo, Olof Mogren
Decentralized deep learning requires dealing with non-iid data across clients, which may also change over time due to temporal shifts.
1 code implementation • 17 Jun 2022 • Edvin Listo Zec, Ebba Ekblom, Martin Willbo, Olof Mogren, Sarunas Girdzijauskas
We study the problem of training personalized deep learning models in a decentralized peer-to-peer setting, focusing on the setting where data distributions differ between the clients and where different clients have different local learning tasks.
1 code implementation • 28 Oct 2021 • Rickard K. A. Karlsson, Martin Willbo, Zeshan Hussain, Rahul G. Krishnan, David Sontag, Fredrik D. Johansson
Our question is when using this privileged data leads to more sample-efficient learning of models that use only baseline data for predictions at test time.