1 code implementation • 13 Mar 2024 • John Martinsson, Olof Mogren, Maria Sandsten, Tuomas Virtanen
In this work we propose an audio recording segmentation method based on an adaptive change point detection (A-CPD) for machine guided weak label annotation of audio recording segments.
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 • 1 Jan 2021 • Edvin Listo Zec, John Martinsson, Olof Mogren, Leon René Sütfeld, Daniel Gillblad
In this paper, we propose a federated learning framework using a mixture of experts to balance the specialist nature of a locally trained model with the generalist knowledge of a global model in a federated learning setting.
1 code implementation • 5 Oct 2020 • Edvin Listo Zec, Olof Mogren, John Martinsson, Leon René Sütfeld, Daniel Gillblad
In federated learning, clients share a global model that has been trained on decentralized local client data.
1 code implementation • 16 Jun 2020 • David Ericsson, Adam Östberg, Edvin Listo Zec, John Martinsson, Olof Mogren
The model is trained in two steps: first to filter sensitive information in the spectrogram domain, and then to generate new and private information independent of the filtered one.
no code implementations • 14 Jun 2020 • John Martinsson, Edvin Listo Zec, Daniel Gillblad, Olof Mogren
Data privacy is an increasingly important aspect of many real-world Data sources that contain sensitive information may have immense potential which could be unlocked using the right privacy enhancing transformations, but current methods often fail to produce convincing output.