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 $A$.

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# 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.

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# Deep TEN: Texture Encoding Network

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

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# 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.

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

1 Mar 2017js3611/Deep-MRI-Reconstruction

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

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# 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.

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

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.

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# 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.

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# 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.

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# 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.

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# 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.

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