Dictionary Learning

118 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 $A$.

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


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Most implemented papers

Deep TEN: Texture Encoding Network

zhanghang1989/Deep-Encoding CVPR 2017

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

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

justinsalamon/UrbanSound8K-JAMS 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.

Convolutional Analysis Operator Learning: Acceleration and Convergence

mechatoz/convolt 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.

When Are Nonconvex Problems Not Scary?

sunju/pr_plain 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.

A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

js3611/Deep-MRI-Reconstruction 1 Mar 2017

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

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

js3611/Deep-MRI-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.

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

ybayle/ISM2017 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.

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

tiepvupsu/DICTOL 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.

Multiple Instance Dictionary Learning using Functions of Multiple Instances

TigerSense/FUMI 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.

Learning a low-rank shared dictionary for object classification

tiepvupsu/DICTOL 31 Jan 2016

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