# Dictionary Learning

117 papers with code • 0 benchmarks • 6 datasets

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 $A$.

## Libraries

Use these libraries to find Dictionary Learning models and implementations
2 papers
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2 papers
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# Deep TEN: Texture Encoding Network

The representation is orderless and therefore is particularly useful for material and texture recognition.

11

# Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

15 Aug 2016

We show that the improved performance stems from the combination of a deep, high-capacity model and an augmented training set: this combination outperforms both the proposed CNN without augmentation and a "shallow" dictionary learning model with augmentation.

5

# Convolutional Analysis Operator Learning: Acceleration and Convergence

15 Feb 2018

This paper proposes a new convolutional analysis operator learning (CAOL) framework that learns an analysis sparsifying regularizer with the convolution perspective, and develops a new convergent Block Proximal Extrapolated Gradient method using a Majorizer (BPEG-M) to solve the corresponding block multi-nonconvex problems.

5

# When Are Nonconvex Problems Not Scary?

21 Oct 2015

In this note, we focus on smooth nonconvex optimization problems that obey: (1) all local minimizers are also global; and (2) around any saddle point or local maximizer, the objective has a negative directional curvature.

3

# A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

1 Mar 2017

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.

3

# A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

8 Apr 2017

Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed.

3

# Sparse Pursuit and Dictionary Learning for Blind Source Separation in Polyphonic Music Recordings

1 Jun 2018

In general, due to its pitch-invariance, our method is especially suitable for dealing with spectra from acoustic instruments, requiring only a minimal number of hyperparameters to be preset.

3

# Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

16 Jun 2015

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures.

2

# Multiple Instance Dictionary Learning using Functions of Multiple Instances

9 Nov 2015

A multiple instance dictionary learning method using functions of multiple instances (DL-FUMI) is proposed to address target detection and two-class classification problems with inaccurate training labels.

2

# Learning a low-rank shared dictionary for object classification

31 Jan 2016

Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns.

2