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Found 2 papers, 1 papers with code
Deep Learning of Compressed Sensing Operators with Structural Similarity Loss
Compressed sensing (CS) is a signal processing framework for efficiently reconstructing a signal from a small number of measurements, obtained by linear projections of the signal.
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks
While mainstream deep learning methods train the neural networks weights while keeping the network architecture fixed, the emerging neural architecture search (NAS) techniques make the latter also amenable to training.
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