An Introduction to Frames

By Jelena Kovacevic, Amina Chebira. 2008

202 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Measuring the tendency of CNNs to Learn Surface Statistical Regularities (2017, background): “Along these lines, [5] obtained promising results by modifying the training regime of SGD by forcing the convolutional layers to be approximate Parseval tight frames [19].”
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey (2018, background): “proposed to do so by controlling the spectral norm of the weight matrices of the networks by parameterizing them with ‘parseval tight frames’ [170], hence the name ‘Parseval’ networks.”
Introduction to the non-asymptotic analysis of random matrices (2010, background): “For an introduction to the concept of frames (Example 21), see [41, 19].”
Foundations of Signal Processing (2014, background): “D, are provided by Heil [43], while more on frames can be found in [14,19,55,56].”
Learning Local Feature Descriptors Using Convex Optimisation (2014, background): “It differs in that the binary code length is higher than the original descriptor dimensionality, and the projection matrix forms a Parseval tight frame [12].”
Image-Adaptive GAN based Reconstruction (2019, background): “Furthermore, this choice of A simplifies the BP step, because A† is simply the Hermitian transpose of A (this property follows from the fact that the subsampled Fourier transform is a tight frame [39]).”
On Singular Values of Random Matrices (2015, background): “[15] More general for coordinate, instead of picking vectors from the canonical basis, we select random vector from a fame.”