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Privacy-Preserving Autoencoder for Collaborative Object Detection
Privacy is a crucial concern in collaborative machine vision where a part of a Deep Neural Network (DNN) model runs on the edge, and the rest is executed on the cloud.
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment
Specifically, to articulate a sampling objective, we first assume a super-resolution (SR) method based on feature graph Laplacian regularization (FGLR) that reconstructs the original high-resolution PC, given 3D points chosen by a sampling matrix $\H$.
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