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Found 4 papers, 0 papers with code
Model-adapted Fourier sampling for generative compressed sensing
We study generative compressed sensing when the measurement matrix is randomly subsampled from a unitary matrix (with the DFT as an important special case).
Learning from few examples: Classifying sex from retinal images via deep learning
Here, we showcase results for the performance of DL on small datasets to classify patient sex from fundus images - a trait thought not to be present or quantifiable in fundus images until recently.
A coherence parameter characterizing generative compressed sensing with Fourier measurements
In Bora et al. (2017), a mathematical framework was developed for compressed sensing guarantees in the setting where the measurement matrix is Gaussian and the signal structure is the range of a generative neural network (GNN).
Deep generative demixing: Recovering Lipschitz signals from noisy subgaussian mixtures
Here, we investigate the subgaussian demixing problem for two Lipschitz signals, with GNN demixing as a special case.
