Shape group Boltzmann machine for simultaneous object segmentation and action classification

Zeng, Xunxun; Chen, Fei; Wang, Meiqing

doi:10.1016/j.patrec.2018.04.014

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…For instance, supervised learning techniques, including neural networks (NN) [9][10][11][12][13][14][15][16][17][18][19], convolutional neural networks (CNN) [20][21][22][23][24][25][26], and recurrent neural networks (RNN) [27][28][29][30][31][32], can be adopted for prediction applications and classification applications in the electronics industries. Unsupervised learning techniques, including restricted Boltzmann machine (RBM) [33,34], deep belief networks (DBN) [35], deep Boltzmann machine (DBM) [36], auto-encoders (AE) [37,38], and denoising auto-encoders (DAE) [39], can be used for denoising and generalization. Furthermore, reinforcement learning techniques, including generative adversarial networks (GANs) [40,41] and deep Q-networks (DQNs) [42], can be used to obtain generative networks and discriminative networks for contesting and optimizing in a zero-sum game framework.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Applications with Practical Measured Results in Electronics Industries

et al. 2020

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This editorial introduces the Special Issue, entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries”, of Electronics. Topics covered in this issue include four main parts: (I) environmental information analyses and predictions, (II) unmanned aerial vehicle (UAV) and object tracking applications, (III) measurement and denoising techniques, and (IV) recommendation systems and education systems. Four papers on environmental information analyses and predictions are as follows: (1) “A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence” by Panapakidis et al.; (2) “Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting” by Wan et al.; (3) “Modeling and Analysis of Adaptive Temperature Compensation for Humidity Sensors” by Xu et al.; (4) “An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform” by Zhang et al. Three papers on UAV and object tracking applications are as follows: (1) “Trajectory Planning Algorithm of UAV Based on System Positioning Accuracy Constraints” by Zhou et al.; (2) “OTL-Classifier: Towards Imaging Processing for Future Unmanned Overhead Transmission Line Maintenance” by Zhang et al.; (3) “Model Update Strategies about Object Tracking: A State of the Art Review” by Wang et al. Five papers on measurement and denoising techniques are as follows: (1) “Characterization and Correction of the Geometric Errors in Using Confocal Microscope for Extended Topography Measurement. Part I: Models, Algorithms Development and Validation” by Wang et al.; (2) “Characterization and Correction of the Geometric Errors Using a Confocal Microscope for Extended Topography Measurement, Part II: Experimental Study and Uncertainty Evaluation” by Wang et al.; (3) “Deep Transfer HSI Classification Method Based on Information Measure and Optimal Neighborhood Noise Reduction” by Lin et al.; (4) “Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets” by Chang et al.; (5) “High-Resolution Image Inpainting Based on Multi-Scale Neural Network” by Sun et al. Two papers on recommendation systems and education systems are as follows: (1) “Deep Learning-Enhanced Framework for Performance Evaluation of a Recommending Interface with Varied Recommendation Position and Intensity Based on Eye-Tracking Equipment Data Processing” by Sulikowski et al. and (2) “Generative Adversarial Network Based Neural Audio Caption Model for Oral Evaluation” by Zhang et al.

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Section: Introductionmentioning

confidence: 99%

Deep Learning Applications with Practical Measured Results in Electronics Industries

et al. 2020

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“…Machine learning methods have been used as the powerful tools for feature detection/extraction and trend estimation/forecasting in the distributed sensor network applications. Supervised machine learning methods, such as neural network (NN), 1–18 convolutional neural network (CNN), 19–35 and recurrent neural network (RNN), 36–47 can be applied to the prediction and classification, while unsupervised machine learning methods, such as restricted Boltzmann machine (RBM), 48 deep belief network (DBN), deep Boltzmann machine (DBM), 49,50 auto-encoder (AE), 51–56 and denoising auto-encoder (DAE), can be utilized for the data denoising and model generalization. Furthermore, reinforcement learning methods, including generative adversarial networks (GANs) 57–60 and deep Q-networks (DQNs), are widely used in tools for generative networks and discriminative networks to optimize the contesting process in a zero-sum...…”

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confidence: 99%