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Found 2 papers, 1 papers with code
Federated Learning for Face Recognition with Gradient Correction
With increasing appealing to privacy issues in face recognition, federated learning has emerged as one of the most prevalent approaches to study the unconstrained face recognition problem with private decentralized data.
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
In this work, we present a regularized graph autoencoder for graph attribute imputation, named MEGAE, which aims at mitigating spectral concentration problem by maximizing the graph spectral entropy.
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