New algorithms compare and improve convolutional dictionary learning methods.
problem Challenges in learning convolutional dictionaries.
method Comprehensive comparison and development of new algorithms.
result Identifies most effective methods for convolutional dictionary learning.
CRsAE auto-encoder recovers convolutional dictionary from noisy signals.
problem Recovering a convolutional dictionary from noisy signals.
method Constrained recurrent sparse auto-encoder (CRsAE) architecture.
result CRsAE successfully recovers the underlying dictionary in the presence of noise.
New online method learns from all images, supports incomplete data.
problem High memory usage and limited training data in batch methods.
method Online convolutional dictionary learning with spatial mask support.
result Improved performance and better scalability with training set size.
This paper studies CSDL's reconstruction risk and finds it consistent in the ultra-sparse setting.
problem The statistical properties of convolutional sparse dictionary learning (CSDL).
method Identifies the minimax convergence rate of CSDL in terms of reconstruction risk, upper bounds the risk of an established CSDL estimator, and proves a matching lower bound.
result Consistency in reconstruction risk is possible precisely in the ultra-sparse setting.
New pursuit algorithm for ML-CSC model with improved stability and dictionary learning.
problem Lack of exact pursuit algorithms and conditions for non-empty model in ML-CSC.
method Projection approach for pursuit algorithm, stability bounds, practical alternatives, online dictionary learning.
result Sound pursuit algorithm and practical dictionary learning for ML-CSC model.
CDPL-Net integrates CNN and DL for better image representation.
problem Improving image representation learning by combining CNN and DL.
method CDPL-Net architecture combining CNN and DPL layers, using l1-norm for sparse representation, and efficient stochastic gradient descent.
result Enhanced performance compared to state-of-the-art methods.
A deep network classifies images by scattering and dictionary learning.
problem Classifying images with high accuracy using deep learning.
method Sparse scattering transform followed by ℓ1 dictionary learning in a deep convolutional network. result Higher classification accuracy than AlexNet on ImageNet dataset.
DeepCAM learns convolutional dictionaries for image processing.
problem Processing high-dimensional signals like images efficiently.
method Introduces a Deep Convolutional Analysis Dictionary Model (DeepCAM) using convolutional dictionaries.
result DeepCAM achieves performance comparable to other methods on single image super-resolution.
Whereas CNNs have demonstrated immense progress in many vision problems, they suffer from a dependence on monumental amounts of labeled training data. On the other hand, dictionary learning does not scale to the size of problems that CNNs can handle, despite being very effective at low-level vision tasks such as denois…
A multi-way factor analysis model is introduced for tensor-variate data of any order. Each data item is represented as a (sparse) sum of Kruskal decompositions, a Kruskal-factor analysis (KFA). KFA is nonparametric and can infer both the tensor-rank of each dictionary atom and the number of dictionary atoms. The model …
CRsAE autoencoder learns dictionary for image denoising and brain spike detection.
problem Sparse dictionary learning for image and neural signal processing.
method CRsAE architecture, alternating E-step and M-step, FISTA for sparse coding, EM-inspired regularization.
result CRsAE outperforms conventional methods in image denoising and spike detection.
Proposes a hierarchical deep generative model for natural images.
problem Analyzing piecewise smooth signals like natural images.
method Hierarchical deep generative model with alternating minimization algorithm.
result Demonstrates the model's representation capabilities and classification performance.
A new stochastic solver improves Convolutional Sparse Coding efficiency.
problem Efficiency and sparsity in Convolutional Sparse Coding.
method Randomized subsampling strategy in spatial domain for online learning.
result Improved execution time with no loss in learning quality.
A new greedy method tackles ℓ0,∞ sparse coding for better image processing.
problem Imbalanced sparsity in ℓ0 and ℓ1 norms for image processing. method Greedy matching pursuit for ℓ0,∞ norm optimization. result Efficient method for ℓ0,∞ sparse coding and dictionary learning. This paper tackles spatio-temporal information preservation in machine learning.
problem Conventional machine learning assumes orthogonal data attributes, disrupting spatio-temporal information.
method Shift-invariant k-means, convolutional dictionary learning, and spatio-temporal hypercomplex encoding schemes are proposed.
result Gabor feature extraction outperforms convolutional dictionary learning in spatio-temporal information preservation.
NOODL solves dictionary and coefficient recovery for online learning.
problem Non-convex optimization challenges in dictionary learning.
method NOODL: Neurally plausible alternating Optimization-based Online Dictionary Learning.
result Exact recovery of dictionary and coefficients at geometric rate.
A parallel algorithm learns efficient Kronecker product dictionaries.
problem Sparse representation of 2D signals like images and hyperspectral data.
method Highly parallelizable algorithm for learning separable dictionaries.
result Competitive sparse representations at lower computational cost.
This work combines deep learning and sparse coding for CT image reconstruction.
problem Improving image quality in low-dose CT scans.
method Sparse signal representation using learned dictionaries, inspired by variational autoencoders and deep learning techniques.
result Regularization with learned dictionaries achieves competitive performance in CT reconstruction.
Novel algorithm learns sparse signal representations over topological spaces.
problem Sparse representation of signals over combinatorial topological spaces.
method Leveraging Hodge theory, the paper embeds topology into a dictionary structure via concatenated sub-dictionaries, each as a polynomial of Hodge Laplacians, and optimizes the dictionary coefficients and sparse signal representation via iterative alternating algorithms.
result Efficiently learned sparse representations and underlying relational structure of topological signals.
This work learns sparse tensor representations using mixtures of separable dictionaries.
problem Learning sparse representations of tensor data with structured models.
method Proposes and explores learning a mixture of separable dictionaries with sufficient conditions for local identifiability.
result Developed computational algorithms for batch and online learning.
Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The availability of abundant training data necessitates the development of efficient, robust and provably good dictionary learning algorithms. Algorithmic stability an…
Proposes DCADL for efficient image classification with reduced complexity.
problem Efficiency and discriminative capability in DL methods for image classification.
method Jointly learns a convolutional analysis dictionary and a universal classifier, reducing time complexity.
result Achieves competitive accuracy with reduced computational cost.
Sparse coding in learned dictionaries has been established as a successful approach for signal denoising, source separation and solving inverse problems in general. A dictionary learning method adapts an initial dictionary to a particular signal class by iteratively computing an approximate factorization of a training …
We consider the problem of sparse coding, where each sample consists of a sparse linear combination of a set of dictionary atoms, and the task is to learn both the dictionary elements and the mixing coefficients. Alternating minimization is a popular heuristic for sparse coding, where the dictionary and the coefficient…
PerCDL learns personalized dictionaries for physiological signals combining global and local structures.
problem Representing datasets with both global and local structures in human physiological signals.
method Personalized Convolutional Dictionary Learning (PerCDL) that combines a global and personalized local dictionary.
result PerCDL effectively learns interpretable representations for human locomotion data.
PUDLE method analyzes and improves unrolled sparse coding networks for dictionary learning.
problem Dictionary learning problem, representing data as a combination of few atoms.
method PUDLE method addresses challenges in unrolled sparse coding networks through theoretical analysis and practical strategies.
result PUDLE method provides conditions for recovering and preserving the support of the latent code, and resolves bias and instability issues.
In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing on weighted graphs, an additional design challenge is to incorporate …
We consider the problem of learning overcomplete dictionaries in the context of sparse coding, where each sample selects a sparse subset of dictionary elements. Our main result is a strategy to approximately recover the unknown dictionary using an efficient algorithm. Our algorithm is a clustering-style procedure, wher…
Paper proposes semi-supervised method for dictionary learning.
problem Learning from both labeled and unlabeled data.
method Uses semi-supervised dictionary learning with LLE for manifold preservation.
result Significant improvements over other methods demonstrated.
A-DLISTA and VLISTA learn dictionaries and sparse representations under varying sensing matrices.
problem Learning dictionaries and sparse representations under varying sensing matrices.
method Augmented Dictionary Learning ISTA (A-DLISTA) and Variational Learning ISTA (VLISTA).
result VLISTA provides a probabilistic way to jointly learn the dictionary distribution and the reconstruction algorithm.
We study the Dictionary Learning (aka Sparse Coding) problem of obtaining a sparse representation of data points, by learning \emph{dictionary vectors} upon which the data points can be written as sparse linear combinations. We view this problem from a geometry perspective as the spanning set of a subspace arrangement,…
A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictionary. While this paradigm has led to numerous empirical successes in various fields ranging from image to audio processing, there have only …
A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a learned dictionary. While this paradigm has led to numerous empirical successes in various fields ranging from image to audio processing, there have only …
The paper provides guarantees for an alternating minimization algorithm in dictionary learning.
problem Dictionary learning problem of factorizing samples into a basis and sparse vectors.
method Alternating minimization procedure switching between ℓ1 minimization and gradient descent. result Local convergence guarantees for the alternating minimization algorithm under a new matrix infinity norm condition.
Performing signal processing tasks on compressive measurements of data has received great attention in recent years. In this paper, we extend previous work on compressive dictionary learning by showing that more general random projections may be used, including sparse ones. More precisely, we examine compressive K-mean…
A new CNN architecture tackles domain shifts with a dictionary approach.
problem Handling domain shifts in deep learning models.
method Decompose CNN layers into domain-specific and shared parts using a dictionary of atoms.
result The approach promotes shared semantics across domains with minimal additional parameters.
Bayesian method improves dictionary learning for complex problems.
problem Efficiently identifying relevant dictionary entries for complex inverse problems.
method Bayesian group sparsity coding and deflation steps to compress and identify relevant subdictionaries.
result Significant computational complexity reduction and improved glitch detection in LIGO experiment.
New algorithms learn sparse dictionaries from incomplete data.
problem Learning dictionaries from incomplete data.
method Iterative descent algorithm with initialization using extra samples.
result Provable polynomial-time algorithms for dictionary learning from incomplete data.
MSBDL learns multimodal dictionaries with flexibility and superior performance.
problem Learning dictionaries for datasets from multiple data sources.
method Multimodal sparse Bayesian dictionary learning (MSBDL) with joint sparsity constraint.
result MSBDL outperforms existing methods on synthetic and real datasets.
GPCDL uses Gaussian Processes to learn smooth templates from data.
problem Lack of smoothness in learned templates leads to overfitting and poor predictive performance.
method GPCDL incorporates Gaussian Process priors to enforce smoothness in the learned templates.
result GPCDL outperforms unregularized CDL in accuracy and predictive performance across various SNRs and applications.
The paper proposes efficient dictionary learning algorithms that avoid multiplications for sparse representations.
problem Sparse representation with reduced computational complexity.
method Factorizations of the dictionary into binary orthonormal, scaling, and shear transformations with closed-form solutions.
result The proposed methods are effective and can be compared to well-known transforms like FFT and DCT.
OnAIR reconstructs dynamic images from sparse measurements online.
problem Reconstructing dynamic images from limited or corrupted measurements.
method Online adaptive reconstruction using sparsity and low-rank models with dictionary learning.
result Memory-efficient online algorithms for sequential estimation of dictionary and images.
New algorithm recovers sparse signals from linearly sparse dictionaries efficiently.
problem Recovering sparse signals from linearly sparse dictionaries.
method Spectral method on reweighted covariance matrices.
result First polynomial-time algorithm for near-linear sparsity in overcomplete dictionaries.
The paper proposes a method to learn discriminative multilevel dictionaries for supervised image classification.
problem Improving sparse representation for supervised image classification.
method Learning structured multilevel dictionaries with discriminative constraints for each class, using reconstruction errors of image patches.
result Competitive results compared to state-of-the-art methods on texture image classification.
Learning optimal dictionaries for sparse coding has exposed characteristic sparse features of many natural signals. However, universal guarantees of the stability of such features in the presence of noise are lacking. Here, we provide very general conditions guaranteeing when dictionaries yielding the sparsest encoding…
Unified analysis for robust PCA decomposition with sparse components in known dictionaries.
problem Robust PCA decomposition with sparse components in known dictionaries.
method Convex demixing method for undercomplete and overcomplete dictionary cases.
result Successful recovery of constituent components up to a certain global sparsity level.
Dictionary learning is the problem of estimating the collection of atomic elements that provide a sparse representation of measured/collected signals or data. This paper finds fundamental limits on the sample complexity of estimating dictionaries for tensor data by proving a lower bound on the minimax risk. This lower …
New algorithm recovers dictionaries with arbitrary supports in polynomial time.
problem Learning dictionaries with arbitrary supports in polynomial time.
method Semirandom model with a mix of arbitrary and random supports; polynomial time algorithm.
result Polynomial time recovery of incoherent over-complete dictionaries with arbitrary supports.