Search Results for author:
Found 6 papers, 3 papers with code
From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning
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.
Impacts of Color and Texture Distortions on Earth Observation Data in Deep Learning
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.
Federated learning using mixture of experts
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.
Specialized federated learning using a mixture of experts
In federated learning, clients share a global model that has been trained on decentralized local client data.
Adversarial representation learning for private speech generation
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.
Adversarial representation learning for synthetic replacement of private attributes
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.
