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Found 11 papers, 1 papers with code
Characterizing the maximum parameter of the total-variation denoising through the pseudo-inverse of the divergence
Though, it is of importance when tuning the regularization parameter as it allows fixing an upper-bound on the grid for which the optimal parameter is sought.
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100$\times$ speed-up
Image restoration methods aim to recover the underlying clean image from corrupted observations.
A unified framework for hard and soft clustering with regularized optimal transport
In this paper, we formulate the problem of inferring a Finite Mixture Model from discrete data as an optimal transport problem with entropic regularization of parameter $\lambda\geq 0$.
Image denoising with generalized Gaussian mixture model patch priors
In this paper, we show that a generalized Gaussian mixture model (GGMM) captures the underlying distribution of patches better than a GMM.
Machine learning in acoustics: theory and applications
Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science.
Block based refitting in $\ell_{12}$ sparse regularisation
This is done through the use of refitting block penalties that only act on the support of the estimated solution.
WaveQ: Gradient-Based Deep Quantization of Neural Networks through Sinusoidal Adaptive Regularization
We show how SINAREQ balance compute efficiency and accuracy, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks (AlexNet, CIFAR-10, MobileNet, ResNet-18, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy.
WAVEQ: GRADIENT-BASED DEEP QUANTIZATION OF NEURAL NETWORKS THROUGH SINUSOIDAL REGULARIZATION
We show how WaveQ balance compute efficiency and accuracy, and provide a heterogeneous bitwidth assignment for quantization of a large variety of deep networks (AlexNet, CIFAR-10, MobileNet, ResNet-18, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy.
Multitemporal SAR images change detection and visualization using RABASAR and simplified GLR
In addition, we apply the simplified generalized likelihood ratio to detect the maximum change magnitude time, and the change starting and ending times.
Patch-based adaptive temporal filter and residual evaluation
Spatial adaptive denoising methods can improve the patch-based weighted temporal average image when the time series is limited.
