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Автор: Grafiati
Опубліковано: 7 липня 2024

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Зміст

  1. Статті в журналах
  2. Дисертації
  3. Частини книг
  4. Тези доповідей конференцій

Статті в журналах з теми "Convolutional Dictionary Learning (CDL)":

1

Li, Jing, Xiao Wei, Fengpin Wang, and Jinjia Wang. "IPGM: Inertial Proximal Gradient Method for Convolutional Dictionary Learning." Electronics 10, no. 23 (December 3, 2021): 3021. http://dx.doi.org/10.3390/electronics10233021.

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Inspired by the recent success of the proximal gradient method (PGM) and recent efforts to develop an inertial algorithm, we propose an inertial PGM (IPGM) for convolutional dictionary learning (CDL) by jointly optimizing both an ℓ2-norm data fidelity term and a sparsity term that enforces an ℓ1 penalty. Contrary to other CDL methods, in the proposed approach, the dictionary and needles are updated with an inertial force by the PGM. We obtain a novel derivative formula for the needles and dictionary with respect to the data fidelity term. At the same time, a gradient descent step is designed to add an inertial term. The proximal operation uses the thresholding operation for needles and projects the dictionary to a unit-norm sphere. We prove the convergence property of the proposed IPGM algorithm in a backtracking case. Simulation results show that the proposed IPGM achieves better performance than the PGM and slice-based methods that possess the same structure and are optimized using the alternating-direction method of multipliers (ADMM).
2

Turquais, Pierre, Endrias G. Asgedom, and Walter Söllner. "A method of combining coherence-constrained sparse coding and dictionary learning for denoising." GEOPHYSICS 82, no. 3 (May 1, 2017): V137—V148. http://dx.doi.org/10.1190/geo2016-0164.1.

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We have addressed the seismic data denoising problem, in which the noise is random and has an unknown spatiotemporally varying variance. In seismic data processing, random noise is often attenuated using transform-based methods. The success of these methods in denoising depends on the ability of the transform to efficiently describe the signal features in the data. Fixed transforms (e.g., wavelets, curvelets) do not adapt to the data and might fail to efficiently describe complex morphologies in the seismic data. Alternatively, dictionary learning methods adapt to the local morphology of the data and provide state-of-the-art denoising results. However, conventional denoising by dictionary learning requires a priori information on the noise variance, and it encounters difficulties when applied for denoising seismic data in which the noise variance is varying in space or time. We have developed a coherence-constrained dictionary learning (CDL) method for denoising that does not require any a priori information related to the signal or noise. To denoise a given window of a seismic section using CDL, overlapping small 2D patches are extracted and a dictionary of patch-sized signals is trained to learn the elementary features embedded in the seismic signal. For each patch, using the learned dictionary, a sparse optimization problem is solved, and a sparse approximation of the patch is computed to attenuate the random noise. Unlike conventional dictionary learning, the sparsity of the approximation is constrained based on coherence such that it does not need a priori noise variance or signal sparsity information and is still optimal to filter out Gaussian random noise. The denoising performance of the CDL method is validated using synthetic and field data examples, and it is compared with the K-SVD and FX-Decon denoising. We found that CDL gives better denoising results than K-SVD and FX-Decon for removing noise when the variance varies in space or time.
3

Song, Andrew, Bahareh Tolooshams, and Demba Ba. "Gaussian Process Convolutional Dictionary Learning." IEEE Signal Processing Letters 29 (2022): 95–99. http://dx.doi.org/10.1109/lsp.2021.3127471.

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4

Gao, Fangyuan, Xin Deng, Mai Xu, Jingyi Xu, and Pier Luigi Dragotti. "Multi-Modal Convolutional Dictionary Learning." IEEE Transactions on Image Processing 31 (2022): 1325–39. http://dx.doi.org/10.1109/tip.2022.3141251.

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5

Chun, Il Yong, and Jeffrey A. Fessler. "Convolutional Dictionary Learning: Acceleration and Convergence." IEEE Transactions on Image Processing 27, no. 4 (April 2018): 1697–712. http://dx.doi.org/10.1109/tip.2017.2761545.

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6

Song, Andrew H., Francisco J. Flores, and Demba Ba. "Convolutional Dictionary Learning With Grid Refinement." IEEE Transactions on Signal Processing 68 (2020): 2558–73. http://dx.doi.org/10.1109/tsp.2020.2986897.

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7

Wan, Fucheng, Dengyun Zhu, Xiangzhen He, Qi Guo, Dongjiao Zhang, Zhenyang Ren, and Yuxiang Du. "Agricultural Product Recommendation Model based on BMF." Applied Mathematics and Nonlinear Sciences 5, no. 2 (July 1, 2020): 415–24. http://dx.doi.org/10.2478/amns.2020.2.00060.

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Abstract In this article, based on the collaborative deep learning (CDL) and convolutional matrix factorisation (ConvMF), the language model BERT is used to replace the traditional word vector construction method, and the bidirectional long–short time memory network Bi-LSTM is used to construct an improved collaborative filtering model BMF, which not only solves the phenomenon of ‘polysemy’, but also alleviates the problem of sparse scoring matrix data. Experiments show that the proposed model is effective and superior to CDL and ConvMF. The trained MSE value is 1.031, which is 9.7% lower than ConvMF.
8

Mansha, Sameen, Hoang Thanh Lam, Hongzhi Yin, Faisal Kamiran, and Mohsen Ali. "Layered convolutional dictionary learning for sparse coding itemsets." World Wide Web 22, no. 5 (May 11, 2018): 2225–39. http://dx.doi.org/10.1007/s11280-018-0565-2.

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9

Molla, Md Rony, and Ma Jian Fen. "Convolutional Sparse Coding Multiple Instance Learning for Whole Slide Image Classification." International Journal of Advanced Engineering Research and Science 10, no. 12 (2023): 096–104. http://dx.doi.org/10.22161/ijaers.1012.10.

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Multiple Instance Learning (MIL) is commonly utilized in weakly supervised whole slide image (WSI) classification. MIL techniques typically involve a feature embedding step using a pretrained feature extractor, then an aggregator that aggregates the embedded instances into predictions. Current efforts aim to enhance these sections by refining feature embeddings through self-supervised pretraining and modeling correlations between instances. In this paper, we propose a convolutional sparsely coded MIL (CSCMIL) that utilizes convolutional sparse dictionary learning to simultaneously address these two aspects. Sparse dictionary learning consists of filters or kernels that are applied with convolutional operations and utilizes an overly comprehensive dictionary to represent instances as sparse linear combinations of atoms, thereby capturing their similarities. Straightforwardly built into existing MIL frameworks, the suggested CSC module has an affordable computation cost. Experiments on various datasets showed that the suggested CSC module improved performance by 3.85% in AUC and 4.50% in accuracy, equivalent to the SimCLR pretraining (4.21% and 4.98%) significantly of current MIL approaches.
10

Humbert, Pierre, Laurent Oudre, Nicolas Vayatis, and Julien Audiffren. "Tensor Convolutional Dictionary Learning With CP Low-Rank Activations." IEEE Transactions on Signal Processing 70 (2022): 785–96. http://dx.doi.org/10.1109/tsp.2021.3135695.

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Статті в журналах

Дисертації з теми "Convolutional Dictionary Learning (CDL)":

1

Allain, Cédric. "Temporal point processes and scalable convolutional dictionary learning : a unified framework for m/eeg signal analysis in neuroscience." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG008.

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Dans le domaine de l'imagerie cérébrale non invasive, la magnéto- et l'électroencéphalographie (M/EEG) offrent un précieux aperçu des activités neuronales. Les données enregistrées consistent en des séries temporelles multivariées qui fournissent des informations sur les processus cognitifs et sont souvent complétées par des détails auxiliaires liés au paradigme expérimental, tels que l'horodatage des stimuli externes ou des actions entreprises par les sujets. En outre, l'ensemble des données peut inclure des enregistrements de plusieurs sujets, ce qui facilite les analyses en population.Cette thèse de doctorat présente un nouveau cadre pour l'analyse des signaux M/EEG qui synergise l'Apprentissage Convolutif de Dictionnaire (CDL) et les Processus Ponctuels Temporels (TPP). Ce travail est divisé en deux composantes principales : les avancées en modélisation temporelle et le passage à l'échelle computationnelle. En matière de modélisation temporelle, deux nouveaux modèles de processus ponctuels sont introduits, accompagnés de méthodes d'inférence efficaces pour capturer les activités neuronales liées aux tâches. La méthode proposée d'Inférence Discrétisée Rapide pour les Processus de Hawkes (FaDIn) a également des implications pour des applications plus larges. De plus, ce travail aborde les défis computationnels de l'analyse des données M/EEG à grande échelle basée sur le CDL, en introduisant un nouvel algorithme robuste de CDL avec fenêtrage stochastique. Cet algorithme permet de traiter efficacement les signaux entachés d'artefacts ainsi que les études de population à grande échelle. Le CDL populationnelle a ensuite été utilisée sur le grand ensemble de données en libre accès Cam-CAN, révélant des aspects de l'activité neuronale liée à l'âge
In the field of non-invasive brain imaging, Magnetoencephalography and Electroencephalography (M/EEG) offer invaluable insights into neural activities. The recorded data consist of multivariate time series that provide information about cognitive processes and are often complemented by auxiliary details related to the experimental paradigm, such as timestamps of external stimuli or actions undertaken by the subjects. Additionally, the dataset may include recordings from multiple subjects, facilitating population- level analyses.This doctoral research presents a novel framework for M/EEG signal analysis that synergizes Convolutional Dictionary Learning (CDL) and Temporal Point Processes (TPPs). The work is segmented into two primary components: temporal modeling advancements and computational scalability. For temporal modeling, two novel point process models are introduced with efficient inference methods to capture task-specific neural activities. The proposed Fast Discretized Inference for Hawkes Processes (FaDIn) method also has implications for broader applications. Additionally, this work addresses the computational challenges of large-scale M/EEG data CDL-based analysis, by introducing a novel Stochastic Robust Windowing CDL algorithm. This algorithm allows to process efficiently artifact-ridden signals as well as large population studies. Population CDL was then used on the large open-access dataset Cam-CAN, shedding light on age-related neural activity
2

Quesada, Pacora Jorge Gerardo. "Separable dictionary learning for convolutional sparse coding via split updates." Master's thesis, Pontificia Universidad Católica del Perú, 2019. http://hdl.handle.net/20.500.12404/14209.

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The increasing ubiquity of Convolutional Sparse Representation techniques for several image processing tasks (such as object recognition and classification, as well as image denoising) has recently sparked interest in the use of separable 2D dictionary filter banks (as alternatives to standard nonseparable dictionaries) for efficient Convolutional Sparse Coding (CSC) implementations. However, existing methods approximate a set of K non-separable filters via a linear combination of R (R << K) separable filters, which puts an upper bound on the latter’s quality. Furthermore, this implies the need to learn first the whole set of non-separable filters, and only then compute the separable set, which is not optimal from a computational perspective. In this context, the purpose of the present work is to propose a method to directly learn a set of K separable dictionary filters from a given image training set by drawing ideas from standard Convolutional Dictionary Learning (CDL) methods. We show that the separable filters obtained by the proposed method match the performance of an equivalent number of non-separable filters. Furthermore, the computational performance of this learning method is shown to be substantially faster than a state-of-the-art non-separable CDL method when either the image training set or the filter set are large. The method and results presented here have been published [1] at the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2018). Furthermore, a preliminary approach (mentioned at the end of Chapter 2) was also published at ICASSP 2017 [2]. The structure of the document is organized as follows. Chapter 1 introduces the problem of interest and outlines the scope of this work. Chapter 2 provides the reader with a brief summary of the relevant literature in optimization, CDL and previous use of separable filters. Chapter 3 presents the details of the proposed method and some implementation highlights. Chapter 4 reports the attained computational results through several simulations. Chapter 5 summarizes the attained results and draws some final conclusions.
Tesis
3

Silva, Obregón Gustavo Manuel. "Efficient algorithms for convolutional dictionary learning via accelerated proximal gradient." Master's thesis, Pontificia Universidad Católica del Perú, 2019. http://hdl.handle.net/20.500.12404/13903.

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Convolutional sparse representations and convolutional dictionary learning are mathematical models that consist in representing a whole signal or image as a sum of convolutions between dictionary filters and coefficient maps. Unlike the patch-based counterparts, these convolutional forms are receiving an increase attention in multiple image processing tasks, since they do not present the usual patchwise drawbacks such as redundancy, multi-evaluations and non-translational invariant. Particularly, the convolutional dictionary learning (CDL) problem is addressed as an alternating minimization between coefficient update and dictionary update stages. A wide number of different algorithms based on FISTA (Fast Iterative Shrinkage-Thresholding Algorithm), ADMM (Alternating Direction Method of Multipliers) and ADMM consensus frameworks have been proposed to efficiently solve the most expensive steps of the CDL problem in the frequency domain. However, the use of the existing methods on large sets of images is computationally restricted by the dictionary update stage. The present thesis report is strategically organized in three parts. On the first part, we introduce the general topic of the CDL problem and the state-of-the-art methods used to deal with each stage. On the second part, we propose our first computationally efficient method to solve the entire CDL problem using the Accelerated Proximal Gradient (APG) framework in both updates. Additionally, a novel update model reminiscent of the Block Gauss-Seidel (BGS) method is incorporated to reduce the number of estimated components during the coefficient update. On the final part, we propose another alternative method to address the dictionary update stage based on APG consensus approach. This last method considers particular strategies of theADMMconsensus and our first APG framework to develop a less complex solution decoupled across the training images. In general, due to the lower number of operations, our first approach is a better serial option while our last approach has as advantage its independent and highly parallelizable structure. Finally, in our first set of experimental results, which is composed of serial implementations, we show that our first APG approach provides significant speedup with respect to the standard methods by a factor of 1:6 5:3. A complementary improvement by a factor of 2 is achieved by using the reminiscent BGS model. On the other hand, we also report that the second APG approach is the fastest method compared to the state-of-the-art consensus algorithm implemented in serial and parallel. Both proposed methods maintain comparable performance as the other ones in terms of reconstruction metrics, such as PSNR, SSIM and sparsity, in denoising and inpainting tasks.
Tesis
4

Moreau, Thomas. "Représentations Convolutives Parcimonieuses -- application aux signaux physiologiques et interpétabilité de l'apprentissage profond." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLN054/document.

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Les représentations convolutives extraient des motifs récurrents qui aident à comprendre la structure locale dans un jeu de signaux. Elles sont adaptées pour l’analyse des signaux physiologiques, qui nécessite des visualisations mettant en avant les informations pertinentes. Ces représentations sont aussi liées aux modèles d’apprentissage profond. Dans ce manuscrit, nous décrivons des avancées algorithmiques et théoriques autour de ces modèles. Nous montrons d’abord que l’Analyse du Spectre Singulier permet de calculer efficacement une représentation convolutive. Cette représentation est dense et nous décrivons une procédure automatisée pour la rendre plus interprétable. Nous proposons ensuite un algorithme asynchrone, pour accélérer le codage parcimonieux convolutif. Notre algorithme présente une accélération super-linéaire. Dans une seconde partie, nous analysons les liens entre représentations et réseaux de neurones. Nous proposons une étape d’apprentissage supplémentaire, appelée post-entraînement, qui permet d’améliorer les performances du réseau entraîné, en s’assurant que la dernière couche soit optimale. Puis nous étudions les mécanismes qui rendent possible l’accélération du codage parcimonieux avec des réseaux de neurones. Nous montrons que cela est lié à une factorisation de la matrice de Gram du dictionnaire. Finalement, nous illustrons l’intérêt de l’utilisation des représentations convolutives pour les signaux physiologiques. L’apprentissage de dictionnaire convolutif est utilisé pour résumer des signaux de marche et le mouvement du regard est soustrait de signaux oculométriques avec l’Analyse du Spectre Singulier
Convolutional representations extract recurrent patterns which lead to the discovery of local structures in a set of signals. They are well suited to analyze physiological signals which requires interpretable representations in order to understand the relevant information. Moreover, these representations can be linked to deep learning models, as a way to bring interpretability intheir internal representations. In this disserta tion, we describe recent advances on both computational and theoretical aspects of these models.First, we show that the Singular Spectrum Analysis can be used to compute convolutional representations. This representation is dense and we describe an automatized procedure to improve its interpretability. Also, we propose an asynchronous algorithm, called DICOD, based on greedy coordinate descent, to solve convolutional sparse coding for long signals. Our algorithm has super-linear acceleration.In a second part, we focus on the link between representations and neural networks. An extra training step for deep learning, called post-training, is introduced to boost the performances of the trained network by making sure the last layer is optimal. Then, we study the mechanisms which allow to accelerate sparse coding algorithms with neural networks. We show that it is linked to afactorization of the Gram matrix of the dictionary.Finally, we illustrate the relevance of convolutional representations for physiological signals. Convolutional dictionary learning is used to summarize human walk signals and Singular Spectrum Analysis is used to remove the gaze movement in young infant’s oculometric recordings

Частини книг з теми "Convolutional Dictionary Learning (CDL)":

1

Sandilya, Mrinmoy, and S. R. Nirmala. "Compressed Sensing MRI Reconstruction Using Convolutional Dictionary Learning and Laplacian Prior." In IOT with Smart Systems, 661–69. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3945-6_65.

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2

Zhang, Chengfang, and Xingchun Yang. "Image Fusion Based on Masked Online Convolutional Dictionary Learning with Surrogate Function Approach." In Advances in Intelligent Systems and Computing, 70–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5887-0_10.

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3

Elbasiony, Reda, Walid Gomaa, and Tetsuya Ogata. "Deep 3D Pose Dictionary: 3D Human Pose Estimation from Single RGB Image Using Deep Convolutional Neural Network." In Artificial Neural Networks and Machine Learning – ICANN 2018, 310–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01424-7_31.

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4

Zhang, Chengfang, and Xingchun Yang. "Visible and Infrared Image Fusion Based on Online Convolutional Dictionary Learning with Sparse Matrix Computation." In Advances in Wireless Communications and Applications, 123–28. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5697-5_15.

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5

Zhang, Chengfang, and Xingchun Yang. "Visible and Infrared Image Fusion Based on Masked Online Convolutional Dictionary Learning with Frequency Domain Computation." In New Developments of IT, IoT and ICT Applied to Agriculture, 177–82. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5073-7_18.

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6

Dong, Qunxi, Jie Zhang, Qingyang Li, Pau M. Thompson, Richard J. Caselli, Jieping Ye, and Yalin Wang. "Multi-task Dictionary Learning Based on Convolutional Neural Networks for Longitudinal Clinical Score Predictions in Alzheimer’s Disease." In Human Brain and Artificial Intelligence, 21–35. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1398-5_2.

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7

"Convolutional Dictionary Learning." In Computer Vision, 214. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_300327.

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Тези доповідей конференцій з теми "Convolutional Dictionary Learning (CDL)":

1

Chun, Il Yong, and Jeffrey A. Fessler. "Convergent convolutional dictionary learning using Adaptive Contrast Enhancement (CDL-ACE): Application of CDL to image denoising." In 2017 International Conference on Sampling Theory and Applications (SampTA). IEEE, 2017. http://dx.doi.org/10.1109/sampta.2017.8024378.

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2

Zhang, Chengfang. "Convolutional Dictionary Learning Using Global Matching Tracking (CDL-GMT): Application to Visible-Infrared Image Fusion." In 2020 4th Annual International Conference on Data Science and Business Analytics (ICDSBA). IEEE, 2020. http://dx.doi.org/10.1109/icdsba51020.2020.00081.

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3

Liu, Jialin, Cristina Garcia-Cardona, Brendt Wohlberg, and Wotao Yin. "Online convolutional dictionary learning." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296573.

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4

Papyan, Vardan, Yaniv Romano, Michael Elad, and Jeremias Sulam. "Convolutional Dictionary Learning via Local Processing." In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017. http://dx.doi.org/10.1109/iccv.2017.566.

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5

Zazo, Javier, Bahareh Tolooshams, Demba Ba, and Harvard John A. Paulson. "Convolutional Dictionary Learning in Hierarchical Networks." In 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2019. http://dx.doi.org/10.1109/camsap45676.2019.9022440.

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6

Garcia-Cardona, Cristina, and Brendt Wohlberg. "Subproblem coupling in convolutional dictionary learning." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296571.

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7

Xu, Lijun, Ying Wang, and Yijia Zhou. "Dictionary learning in convolutional sparse representation." In Second International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2022), edited by Shi Jin and Wanyang Dai. SPIE, 2023. http://dx.doi.org/10.1117/12.2672444.

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8

Garcia-Cardona, Cristina, and Brendt Wohlberg. "Convolutional Dictionary Learning for Multi-Channel Signals." In 2018 52nd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2018. http://dx.doi.org/10.1109/acssc.2018.8645108.

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9

Skau, Erik, and Cristina Garcia-Cardona. "Tomographic Reconstruction Via 3D Convolutional Dictionary Learning." In 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP). IEEE, 2018. http://dx.doi.org/10.1109/ivmspw.2018.8448657.

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10

Degraux, Kevin, Ulugbek S. Kamilov, Petros T. Boufounos, and Dehong Liu. "Online convolutional dictionary learning for multimodal imaging." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296555.

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