About

Dictionary Learning is an important problem in multiple areas, ranging from computational neuroscience, machine learning, to computer vision and image processing. The general goal is to find a good basis for given data. More formally, in the Dictionary Learning problem, also known as sparse coding, we are given samples of a random vector $y\in\mathbb{R}^n$, of the form $y=Ax$ where $A$ is some unknown matrix in $\mathbb{R}^{n×m}$, called dictionary, and $x$ is sampled from an unknown distribution over sparse vectors. The goal is to approximately recover the dictionary $A$.

Source: Polynomial-time tensor decompositions with sum-of-squares

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Latest papers without code

Locality Constrained Analysis Dictionary Learning via K-SVD Algorithm

29 Apr 2021

With the learned analysis dictionary, test samples can be transformed into a sparse subspace for classification efficiently.

DICTIONARY LEARNING GENERAL CLASSIFICATION IMAGE CLASSIFICATION

Deep Transform and Metric Learning Networks

21 Apr 2021

Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest.

DICTIONARY LEARNING METRIC LEARNING

Learning Log-Determinant Divergences for Positive Definite Matrices

13 Apr 2021

There exist several similarity measures for comparing SPD matrices with documented benefits.

DICTIONARY LEARNING

Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction

11 Apr 2021

We also compare the proposed method to alternative approaches for combining dictionary-based methods with supervised learning in MR image reconstruction.

DICTIONARY LEARNING IMAGE RECONSTRUCTION

Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysis

1 Apr 2021

In this paper, we propose a deep dictionary learning approach to solve the problem of tissue phenotyping in histology images.

DICTIONARY LEARNING

Gaussian Process Convolutional Dictionary Learning

28 Mar 2021

Convolutional dictionary learning (CDL), the problem of estimating shift-invariant templates from data, is typically conducted in the absence of a prior/structure on the templates.

DICTIONARY LEARNING GAUSSIAN PROCESSES

Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond

26 Mar 2021

Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements.

DICTIONARY LEARNING IMAGE RECONSTRUCTION

Exact Sparse Orthogonal Dictionary Learning

14 Mar 2021

Over the past decade, learning a dictionary from input images for sparse modeling has been one of the topics which receive most research attention in image processing and compressed sensing.

DENOISING DICTIONARY LEARNING

An unsupervised deep learning framework for medical image denoising

11 Mar 2021

This paper introduces an unsupervised medical image denoising technique that learns noise characteristics from the available images and constructs denoised images.

DICTIONARY LEARNING IMAGE DENOISING MEDICAL IMAGE DENOISING