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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Datasets

Greatest papers with code

Deep TEN: Texture Encoding Network

CVPR 2017 zhanghang1989/PyTorch-Encoding

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

DICTIONARY LEARNING MATERIAL RECOGNITION

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

15 Aug 2016makcedward/nlpaug

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.

DATA AUGMENTATION DICTIONARY LEARNING ENVIRONMENTAL SOUND CLASSIFICATION

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

8 Apr 2017js3611/Deep-MRI-Reconstruction

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.

DICTIONARY LEARNING IMAGE RECONSTRUCTION

Recognizing Partial Biometric Patterns

17 Oct 2018lingxiao-he/Partial-Person-ReID

Biometric recognition on partial captured targets is challenging, where only several partial observations of objects are available for matching.

DICTIONARY LEARNING FACE RECOGNITION PERSON RE-IDENTIFICATION

Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach

CVPR 2018 lingxiao-he/Partial-Person-ReID

Experimental results on two partial person datasets demonstrate the efficiency and effectiveness of the proposed method in comparison with several state-of-the-art partial person re-id approaches.

DICTIONARY LEARNING PERSON RE-IDENTIFICATION

Fast Low-rank Shared Dictionary Learning for Image Classification

27 Oct 2016tiepvupsu/DICTOL

Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries.

DICTIONARY LEARNING IMAGE CLASSIFICATION

Learning a low-rank shared dictionary for object classification

31 Jan 2016tiepvupsu/DICTOL

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

DICTIONARY LEARNING OBJECT CLASSIFICATION

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

16 Jun 2015tiepvupsu/DICTOL

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.

DICTIONARY LEARNING HISTOPATHOLOGICAL IMAGE CLASSIFICATION IMAGE CLASSIFICATION

Stochastic Subsampling for Factorizing Huge Matrices

19 Jan 2017arthurmensch/modl

We present a matrix-factorization algorithm that scales to input matrices with both huge number of rows and columns.

DICTIONARY LEARNING