Abstract: Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is ...
We introduce DINOv2 SALAD, a Visual Place Recognition model that achieves state-of-the-art results on common benchmarks. We introduce two main contributions: Using a finetuned DINOv2 encoder to get ...
Abstract: Federated learning is useful when predicting user preferences due to its ability to keep user data private. As such, certain data samples may be more useful than others. For instance, with a ...
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