Convolutional Restricted Boltzmann Machine Research Articles

Convolutional restricted Boltzmann machine correlated variational wave function for the Hubbard model on a square lattice

Convolutional restricted Boltzmann machine correlated variational wave function for the Hubbard model on a square lattice

Postoperative facial prediction for mandibular defect based on surface mesh deformation

Objectives: This study aims to introduce a novel predictive model for the post-operative facial contours of patients with mandibular defect, addressing limitations in current methodologies that fail to preserve geometric features and lack interpretability.Methods: Utilizing surface mesh theory and deep learning, our model diverges from traditional point cloud approaches by employing surface triangular mesh grids. We extract latent variables using a Mesh Convolutional Restricted Boltzmann Machines (MCRBM) model to generate a three-dimensional deformation field, aiming to enhance geometric information preservation and interpretability.Results: Experimental evaluations of our model demonstrate a prediction accuracy of 91.2 %, which represents a significant improvement over traditional machine learning-based methods.Conclusions: The proposed model offers a promising new tool for pre-operative planning in oral and maxillofacial surgery. It significantly enhances the accuracy of post-operative facial contour predictions for mandibular defect reconstructions, providing substantial advancements over previous approaches.

Violence detection in crowd videos using nuanced facial expression analysis

Video analysis for violence detection is crucial, especially when dealing with crowd data, where the potential for severe mob attacks in sensitive areas is high. This paper proposes a solution utilizing Convolutional Restricted Boltzmann Machine (CRBM) for video analysis, integrating the strengths of Convolutional Neural Network (CNN) and Restricted Boltzmann Machine (RBM). By focusing on image patches rather than entire frames, the method addresses the challenge of object detection in crowded scenes. The CRBM combines deep-level image analysis from CNN with unsupervised feature extraction in RBM, facilitated by image convolution using Gabor filters in the hidden layer. Dropout regularization mitigates overfitting, enhancing model generality. Extracted features are inputted into an SVM classifier for face detection and a custom VGG16 model for emotion identification. Event probability is then determined through logistic regression based on facial expressions. Despite existing approaches for smart crowd behaviour identification, there remains a tradeoff between accuracy and processing time. Our proposed solution addresses this by employing proper frame preprocessing techniques for feature extraction. Validation using quantitative and qualitative metrics confirms the effectiveness of the approach.

Event-Based Epileptic Seizure Detection with Stacked Convolutional Restricted Boltzmann Machine

Research indicates that around 1% of the global population experiences epileptic seizures, described as an undue neuronal discharge in the brain, which can significantly impact the quality of life for those affected. Children who experience frequent or prolonged seizures may experience lasting cognitive damage and psychological difficulties, and sudden seizures can pose a serious risk of injury or even death. Therefore, developing an accurate machine learning model to predict seizures is crucial. This study introduces an Event-based Stacked Convolutional Restricted Boltzmann Machine architecture that can detect ictal periods in EEG signals. The model was trained and evaluated on a publicly available dataset of EEG recordings from 24 pediatric patients, achieving a high prediction accuracy of 94.2%, with a mean prediction time of 19.62 min. The model's mean sensitivity and specificity rates were also comparable to those of state-of-the-art predictive models on the CHB-MIT dataset.

A Centered Convolutional Restricted Boltzmann Machine Optimized by Hybrid Atom Search Arithmetic Optimization Algorithm for Sentimental Analysis

A Centered Convolutional Restricted Boltzmann Machine Optimized by Hybrid Atom Search Arithmetic Optimization Algorithm for Sentimental Analysis

Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems

Millimeter-wave massive multiple-input multiple-output is a key technology in 5G communication system. In particular, the hybrid precoding method has the advantages of being power efficient and less expensive than the full-digital precoding method, so it has attracted more and more attention. The effectiveness of this method in simple systems has been well verified, but its performance is still unknown due to many problems in real communication such as interference from other users and base stations, and users are constantly on the move. In this article, we propose a dynamic user clustering hybrid precoding method in the high-dimensional millimeter-wave multiple-input multiple-output system, which uses low-dimensional manifolds to avoid complicated calculations when there are many antennas. We model each user set as a novel Convolutional Restricted Boltzmann Machine manifold, and the problem is transformed into cluster-oriented multi-manifold learning. The novel Convolutional Restricted Boltzmann Machine manifold learning seeks to learn embedded low-dimensional manifolds through manifold learning in the face of user mobility in clusters. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. This algorithm avoids the traditional method of processing high-dimensional channel parameters, achieves a high signal-to-noise ratio, and reduces computational complexity. The simulation result table shows that this method can get almost the best summation rate and higher spectral efficiency compared with the traditional method.

Pain detection using batch normalized discriminant restricted Boltzmann machine layers

A system for automatic pain detection whereby pain-related features are extracted from facial images using a four-layer Convolutional Deep Belief Network (CDBN) is proposed in this study. The CDBN is trained by greedy layer-wise procedure whereby each added layer is trained as a Convolutional Restricted Boltzmann Machine (CRBM) by contrastive divergence. Since conventional CRBM is trained in a purely unsupervised manner, there is no guarantee that learned features are appropriate for the supervised task at hand. A discriminative objective based on between-class and within-class distances is proposed to adapt CRBM to learn task-related features. When discriminative and generative objectives are appropriately combined, a competitive classification performance can be achieved. Moreover, we introduced batch normalization (BN) units in the structure of the CRBM model to smooth optimization landscape and speed up the learning process. BN units come right before sigmoid units. Extracted features are then used to train a linear SVM to classify each frame into pain or no-pain classes. Extensive experiments on UNBC-McMaster Shoulder Pain database demonstrate the effectiveness of the proposed method for automatic pain detection.

Centered convolutional deep Boltzmann machine for 2D shape modeling

An object shape information plays a vital role in many computer applications. Among these applications, some tasks can allow object shape analysis directly solve the problem. Thus, how to extract shape features and model the shape is a crucial issue. This paper proposes a new shape modeling method utilizing the centered convolutional deep Boltzmann machine to model two-dimensional (2D) shape. The proposed method employs a probabilistic generative model based on deep Boltzmann machine and convolution computation to extract both local and global features of the shape within one framework. In addition, we also propose a bidirectional inference-based training algorithm coupled with the centering method to better learn the probability distribution of shapes without the need for a pre-training procedure. Our experimental results show that the proposed model can achieve best shape modeling performance in qualitative and quantitative evaluation compared with other probabilistic generative models, including the restricted Boltzmann machine, convolutional restricted Boltzmann machine, and deep Boltzmann machine.

Flexible human motion transition via hybrid deep neural network and quadruple-like structure learning

Skeletal motion transition is of crucial importance to the animation creation. In this paper, we propose a hybrid deep learning framework that allows for efficient human motion transition. First, we integrate a convolutional restricted Boltzmann machine with deep belief network to extract the spatio-temporal features of each motion style, featuring on appropriate detection of transition points. Then, a quadruples-like data structure is exploited for motion graph building, motion splitting and indexing. Accordingly, the similar frames fulfilling the transition segments can be efficiently retrieved. Meanwhile, the transition length is reasonably computed according to the average speed of the motion joints. As a result, different kinds of diverse motions can be well transited with satisfactory performance. The experimental results show that the proposed transition approach brings substantial improvements over the state-of-the-art methods.

Convolutional restricted Boltzmann machine aided Monte Carlo: An application to Ising and Kitaev models

Machine learning is becoming widely used in analyzing the thermodynamics of many-body condensed matter systems. Restricted Boltzmann Machine (RBM) aided Monte Carlo simulations have sparked interest recently, as they manage to speed up classical Monte Carlo simulations. Here we employ the Convolutional Restricted Boltzmann Machine (CRBM) method and show that its use helps to reduce the number of parameters to be learned drastically by taking advantage of translation invariance. Furthermore, we show that it is possible to train the CRBM at smaller lattice sizes, and apply it to larger lattice sizes. To demonstrate the efficiency of CRBM we apply it to the paradigmatic Ising and Kitaev models in two-dimensions.

Image recognition based on improved convolutional deep belief network model

Aiming at the homogeneity of convolution kernels in Convolutional Deep Belief Network (CDBN), a cross-entropy-based sparse penalty mechanism suitable for Convolutional Restricted Boltzmann Machine (CRBM) model is introduced which makes the hidden layer units of the whole network in a lower activation state. On this basis, a parameter learning algorithm is applied to compensate the gradient by introducing the prior information of the samples, which alternates the supervised learning and unsupervised learning. The experimental results show that the proposed model can weaken the homogeneity of convolution kernels and improve the supervising and predicting ability of the network. The recognition rate on simulated dataset achieves 97.45%, which is increased 5.12% and 1.29% than Convolutional Neural Network (CNN) and the traditional CDBN model, respectively. At the same time, test error rate on common dataset MNIST also shows that the proposed model is more effective than some other state-of-the-art deep learning models.

A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.

The optimization of parallel convolutional RBM based on Spark

With the extensive attention and research of the scholars in deep learning, the convolutional restricted Boltzmann machine (CRBM) model based on restricted Boltzmann machine (RBM) is widely used in image recognition, speech recognition, etc. However, time consuming training still seems to be an unneglectable issue. To solve this problem, this paper mainly uses optimized parallel CRBM based on Spark, and proposes a parallel comparison divergence algorithm based on Spark and uses it to train the CRBM model to improve the training speed. The experiments show that the method is faster than traditional sequential algorithm. We train the CRBM with the method and apply it to breast X-ray image classification. The experiments show that it can improve the precision and the speed of training compared with traditional algorithm.

Three-dimensional convolutional restricted Boltzmann machine for human behavior recognition from RGB-D video

This paper provides a novel approach for recognizing human behavior from RGB-D video data. The three-dimensional convolutional restricted Boltzmann machine (3DCRBM) is proposed which can extract features from the raw RGB-D data. In a physical model, the 3DCRBM differs from the restricted Boltzmann machine (RBM) as its weights are shared among all locations in the input and preserving spatial locality. Adjacent frames of the RGB image and the corresponding adjacent frames of the depth image are set as the input of 3DCRBM. Then, multiple 3D convolutional kernels can be applied to these four frames to extract spatio-temporal features. In the experiment of human behavior recognition, the deep belief network (DBN) is established by a layer of 3DCRBM network, convolutional neural network (CNN), and back propagation (BP) network. 3DCRBM is adapted for unsupervised training and getting a feature, while CNN and BP are used for supervised training and classifying the human behavior. The experiment results demonstrate that the correct differentiation rate of 95.7% is achieved, so the effectiveness of our approach could be validated.

Contractive Slab and Spike Convolutional Deep Belief Network

Convolutional Deep Belief Network (CDBN) is typically classified into deep generative model. Although CDBN has demonstrated the powerful capacity of feature extraction in unsupervised learning, there still remain diverse challenges in the robust and high-quality feature extraction. This paper designs an advanced hierarchical generative model in order to tackle with these troubles. First, we modify conventional Convolutional Restricted Boltzmann Machine (CRBM) through inducing Gaussian hidden units subsequently following point-wise multiplication with the original binary spike hidden units for high-order feature extraction of the local patch. We theoretically derive entire inferences of this novel model. Second, we attempt to learn more robust features by minimizing L2 norm of the jacobian of the extracted features producing from the modified model as novel regularization trick. This can introduce a localized space contraction benefit for robust feature extraction in turn. Finally, this paper construct a novel deep generative model, Contractive Slab and Spike Convolutional Deep Belief Network (CssCDBN), based on the modified CRBM, in order to learn deeper and more abstract features. The performances on diverse visual tasks indicate that CssCDBN is a more powerful model achieving impressive results over many currently excellent models.

Detection of weak transient signals based on unsupervised learning for bearing fault diagnosis

Abstract Transient impulse contains abundant information of bearings status. When fault occurs, it is activated and would recur periodically or quasi-periodically. Its period can indicate where defects lie in. However, transient impulse is easily swallowed by background noise or interferences in part or in whole, especially at early stage of fault. This problem brings hard obstacles into faults detection. Considering that transient impulses are periodical or quasi-periodical and vibration signal has local similarity, the single transient impulse can be seen as one of shift-invariant features. In view of this, this paper derives adaptive and non-linear signal decomposition formulas and further proposes adaptive and unsupervised feature learning method by using convolutional restricted Boltzmann machine model. With respecting local waveform structures, this method can automatically capture shift-invariant patterns hidden in original signal and decompose the original signal into several sub-components at the cost of minimizing reconstruction error. Among these sub-components, the fault-related information, i.e., transient impulses signal, could be extracted likely. It provides a promising idea for intelligent signal processing by using unsupervised learning. Afterwards, Maximizing kurtosis is applied to select optimally latent fault component. Two real bearing experiments validate this method is effective and reliable in extraction of weak transient impulses.

Imposing Higher-Level Structure in Polyphonic Music Generation Using Convolutional Restricted Boltzmann Machines and Constraints

We introduce a method for imposing higher-level structure on generated, polyphonic music. A Convolutional Restricted Boltzmann Machine (C-RBM) as a generative model is combined with gradient descent constraint optimisation to provide further control over the generation process. Among other things, this allows for the use of a "template" piece, from which some structural properties can be extracted, and transferred as constraints to the newly generated material. The sampling process is guided with Simulated Annealing to avoid local optima, and to find solutions that both satisfy the constraints, and are relatively stable with respect to the C-RBM. Results show that with this approach it is possible to control the higher-level self-similarity structure, the meter, and the tonal properties of the resulting musical piece, while preserving its local musical coherence.

Contractive Slab and Spike Convolutional Deep Boltzmann Machine

Abstract Deep unsupervised learning for robust and effective feature extractions from high-resolution images still keeps greatly challenging. Although Deep Boltzmann Machine (DBM) has demonstrated the impressive capacity of feature extractions, there is still a lot of potential for improvement in scaling such model to full-sized images, the robustness, and the quality of the learned features. In this paper, we propose a Contractive Slab and Spike Convolutional Deep Boltzmann Machine in order to settle these issues. First, the proposed model extends convolution operation to the DBM in order to deal with real-size images. Second, we induce element-wise multiplication between real-valued slab hidden units and binary spike hidden units in order to enhance the quality of feature extraction in the receptive field. Then, we add the frobenius norm of the jacobian of the features as a regularization term to the maximum likelihood function in order to enhance the robustness of the features during training. The proposed regularization term results in a localized space contraction, which in turn obtains robust features on the hidden layer. Last, we use a new block-Contractive Slab and Spike Convolutional Restricted Boltzmann Machine in order to pretrain the proposed model. The proposed deep model shows the better capacity to extract high-level representations. The results on various visual tasks demonstrate the proposed model can achieve the improved performance over several state-of-the-art methods.

Shape Modeling Based on Convolutional Restricted Boltzmann Machines

This paper proposes a kind of shape model based on convolutional restricted Boltzmann machines(CRBM), which can be used to assist the task of image target detection and classification. The CRBM is a generative model that can model shapes through the generative capabilities of the model. This paper presents the visual representation, construction process and training method of the model construction. This paper does experiments on the Weizmann Horse dataset. The results show that, compared with RBM, although the training time of this model is slightly longer, the test time of the model is similar, and it can better shape modeling, modeling of the details of the shape can be well expressed. The samples generated from CRBM look more realistic. The difference between the shape and the original shape generated by Euclidean distance measurement shows that the model has a strong ability to model shapes.

Learning Multimodal Deep Representations for Crowd Anomaly Event Detection

Anomaly event detection in crowd scenes is extremely important; however, the majority of existing studies merely use hand-crafted features to detect anomalies. In this study, a novel unsupervised deep learning framework is proposed to detect anomaly events in crowded scenes. Specifically, low-level visual features, energy features, and motion map features are simultaneously extracted based on spatiotemporal energy measurements. Three convolutional restricted Boltzmann machines are trained to model the mid-level feature representation of normal patterns. Then a multimodal fusion scheme is utilized to learn the deep representation of crowd patterns. Based on the learned deep representation, a one-class support vector machine model is used to detect anomaly events. The proposed method is evaluated using two available public datasets and compared with state-of-the-art methods. The experimental results show its competitive performance for anomaly event detection in video surveillance.