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Found 10 papers, 2 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).
Artificial intelligence as a gateway to scientific discovery: Uncovering features in retinal fundus images
Here we propose a methodology for explainable classification of fundus images to uncover the mechanism(s) by which CNNs successfully predict the labels.
PLUGIn: A simple algorithm for inverting generative models with recovery guarantees
We prove that, when weights are Gaussian and layer widths $n_i \gtrsim 5^i n_0$ (up to log factors), the algorithm converges geometrically to a neighbourhood of $x$ with high probability.
PLUGIn-CS: A simple algorithm for compressive sensing with generative prior
After a sufficient number of iterations, the estimation errors for both $x$ and $\mathcal{G}(x)$ are at most in the order of $\sqrt{4^dn_0/m} \|\epsilon\|$.
NBIHT: An Efficient Algorithm for 1-bit Compressed Sensing with Optimal Error Decay Rate
The Binary Iterative Hard Thresholding (BIHT) algorithm is a popular reconstruction method for one-bit compressed sensing due to its simplicity and fast empirical convergence.
Information Theory Numerical Analysis Information Theory Numerical Analysis 94-XX
Learning tensors from partial binary measurements
In this paper we generalize the 1-bit matrix completion problem to higher order tensors.
Statistics Theory Information Theory Information Theory Optimization and Control Statistics Theory 62B10, 94A17, 15A69, 62D05 H.3.3; I.2.6
Multi-View Product Image Search Using Deep ConvNets Representations
We concluded that (1) multi-view queries with deep ConvNets representations perform significantly better than single view queries, (2) ConvNets perform much better than BoWs and have room for further improvement, (3) pre-training of ConvNets on a different image dataset with background clutter is needed to obtain good performance on cluttered product image queries obtained with a mobile phone.
U-CATCH: Using Color ATtribute of image patCHes in binary descriptors
In this study, we propose a simple yet very effective method for extracting color information through binary feature description framework.
Classification of Occluded Objects using Fast Recurrent Processing
Recurrent neural networks are powerful tools for handling incomplete data problems in computer vision, thanks to their significant generative capabilities.
