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Found 4 papers, 2 papers with code
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.
Concept-aware clustering for decentralized deep learning under temporal shift
Decentralized deep learning requires dealing with non-iid data across clients, which may also change over time due to temporal shifts.
Decentralized adaptive clustering of deep nets is beneficial for client collaboration
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.
Using Time-Series Privileged Information for Provably Efficient Learning of Prediction Models
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.
