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Статті в журналах з теми "Convolutional Auto-Encoder"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Song, Xiaona, Haichao Liu, Lijun Wang, Song Wang, Yunyu Cao, Donglai Xu, and Shenfeng Zhang. "A Semantic Segmentation Method for Road Environment Images Based on Hybrid Convolutional Auto-Encoder." Traitement du Signal 39, no. 4 (August 31, 2022): 1235–45. http://dx.doi.org/10.18280/ts.390416.

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Анотація:

Deep convolutional neural networks (CNNs) have presented amazing performance in the task of semantic segmentation. However, the network model is complex, the training time is prolonged, the semantic segmentation accuracy is not high and the real-time performance is not good, so it is difficult to be directly used in the semantic segmentation of road environment images of autonomous vehicles. As one of the three models of deep learning, the auto-encoder (AE) has powerful data learning and feature extracting capabilities from the raw data itself. In this study, the network architecture of auto-encoder and convolutional auto-encoder (CAE) is improved, supervised learning auto-encoder and improved convolutional auto-encoder are proposed, and a hybrid convolutional auto-encoder model is constructed by combining them. It can extract low-dimensional abstract features of road environment images by using convolution layers and pooling layers in front of the network, and then supervised learning auto-encoder are used to enhance and express semantic segmentation features, and finally de-convolution layers and un-pooling layers are used to generate semantic segmentation results. The hybrid convolutional auto-encoder model proposed in this paper not only contains encoding and decoding parts which are used in the common semantic segmentation models, but also adds semantic feature enhancing and representing parts, so that the network which has fewer convolutional and pooling layers can still achieve better semantic segmentation effects. Compared to the semantic segmentation based on convolutional neural networks, the hybrid convolutional auto-encoder has fewer network layers, fewer network parameters, and simpler network training. We evaluated our proposed method on Camvid and Cityscapes, which are standard benchmarks for semantic segmentation, and it proved to have a better semantic segmentation effect and good real-time performance.

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2

Kim, Dong-Hoon, JoonWhoan Lee, and #VALUE! #VALUE! "Music Mood recognition using Convolutional Variation Auto Encoder." Journal of Korean Institute of Intelligent Systems 29, no. 5 (October 31, 2019): 352–58. http://dx.doi.org/10.5391/jkiis.2019.29.5.352.

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3

Theunissen, Carl Daniel, Steven Martin Bradshaw, Lidia Auret, and Tobias Muller Louw. "One-Dimensional Convolutional Auto-Encoder for Predicting Furnace Blowback Events from Multivariate Time Series Process Data—A Case Study." Minerals 11, no. 10 (October 9, 2021): 1106. http://dx.doi.org/10.3390/min11101106.

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Modern industrial mining and mineral processing applications are characterized by large volumes of historical process data. Hazardous events occurring in these processes compromise process safety and therefore overall viability. These events are recorded in historical data and are often preceded by characteristic patterns. Reconstruction-based data-driven models are trained to reconstruct the characteristic patterns of hazardous event-preceding process data with minimal residuals, facilitating effective event prediction based on reconstruction residuals. This investigation evaluated one-dimensional convolutional auto-encoders as reconstruction-based data-driven models for predicting positive pressure events in industrial furnaces. A simple furnace model was used to generate dynamic multivariate process data with simulated positive pressure events to use as a case study. A one-dimensional convolutional auto-encoder was trained as a reconstruction-based model to recognize the data preceding the hazardous events, and its performance was evaluated by comparing it to a fully-connected auto-encoder as well as a principal component analysis reconstruction model. This investigation found that one-dimensional convolutional auto-encoders recognized event-preceding patterns with lower detection delays, higher specificities, and lower missed alarm rates, suggesting that the one-dimensional convolutional auto-encoder layout is superior to the fully connected auto-encoder layout for use as a reconstruction-based event prediction model. This investigation also found that the nonlinear auto-encoder models outperformed the linear principal component model investigated. While the one-dimensional auto-encoder was evaluated comparatively on a simulated furnace case study, the methodology used in this evaluation can be applied to industrial furnaces and other mineral processing applications. Further investigation using industrial data will allow for a view of the convolutional auto-encoder’s absolute performance as a reconstruction-based hazardous event prediction model.

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4

Yasukawa, Shinsuke, Sreeraman Raghura, Yuya Nishida, and Kazuo Ishii. "Underwater image reconstruction using convolutional auto-encoder." Proceedings of International Conference on Artificial Life and Robotics 26 (January 21, 2021): 262–65. http://dx.doi.org/10.5954/icarob.2021.os23-4.

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5

Zhao, Wei, Zuchen Jia, Xiaosong Wei, and Hai Wang. "An FPGA Implementation of a Convolutional Auto-Encoder." Applied Sciences 8, no. 4 (March 27, 2018): 504. http://dx.doi.org/10.3390/app8040504.

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6

Li, Hongfei, Lili Meng, Jia Zhang, Yanyan Tan, Yuwei Ren, and Huaxiang Zhang. "Multiple Description Coding Based on Convolutional Auto-Encoder." IEEE Access 7 (2019): 26013–21. http://dx.doi.org/10.1109/access.2019.2900498.

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7

Newlin, Dev R., and C. Seldev Christopher. "De-noising of Natural Images with Better Enhancement Using Convolutional Auto-Encoder." Journal of Advanced Research in Dynamical and Control Systems 11, no. 12 (December 20, 2019): 124–36. http://dx.doi.org/10.5373/jardcs/v11i12/20193221.

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8

Zhu, Yi, Lei Li, and Xindong Wu. "Stacked Convolutional Sparse Auto-Encoders for Representation Learning." ACM Transactions on Knowledge Discovery from Data 15, no. 2 (April 2021): 1–21. http://dx.doi.org/10.1145/3434767.

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Анотація:

Deep learning seeks to achieve excellent performance for representation learning in image datasets. However, supervised deep learning models such as convolutional neural networks require a large number of labeled image data, which is intractable in applications, while unsupervised deep learning models like stacked denoising auto-encoder cannot employ label information. Meanwhile, the redundancy of image data incurs performance degradation on representation learning for aforementioned models. To address these problems, we propose a semi-supervised deep learning framework called stacked convolutional sparse auto-encoder, which can learn robust and sparse representations from image data with fewer labeled data records. More specifically, the framework is constructed by stacking layers. In each layer, higher layer feature representations are generated by features of lower layers in a convolutional way with kernels learned by a sparse auto-encoder. Meanwhile, to solve the data redundance problem, the algorithm of Reconstruction Independent Component Analysis is designed to train on patches for sphering the input data. The label information is encoded using a Softmax Regression model for semi-supervised learning. With this framework, higher level representations are learned by layers mapping from image data. It can boost the performance of the base subsequent classifiers such as support vector machines. Extensive experiments demonstrate the superior classification performance of our framework compared to several state-of-the-art representation learning methods.

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9

Zhou, Jian, Xianwei Wei, Chunling Cheng, Qidong Yang, and Qun Li. "Multimodal Emotion Recognition Method Based on Convolutional Auto-Encoder." International Journal of Computational Intelligence Systems 12, no. 1 (2019): 351. http://dx.doi.org/10.2991/ijcis.2019.125905651.

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10

Oh, Junghyun, and Beomhee Lee. "Condition-invariant Place Recognition Using Deep Convolutional Auto-encoder." Journal of Korea Robotics Society 14, no. 1 (March 30, 2019): 8–13. http://dx.doi.org/10.7746/jkros.2019.14.1.008.

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11

Yang, Haoyu, Shuhe Li, Wen Chen, Piyush Pathak, Frank Gennari, Ya-Chieh Lai, and Bei Yu. "DeePattern: Layout Pattern Generation With Transforming Convolutional Auto-Encoder." IEEE Transactions on Semiconductor Manufacturing 35, no. 1 (February 2022): 67–77. http://dx.doi.org/10.1109/tsm.2021.3139354.

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12

Zhang Xiu, 张. 秀., 周. 巍. Zhou Wei, 段哲民 Duan Zhemin, and 魏恒璐 Wei Henglu. "Convolutional sparse auto-encoder for image super-resolution reconstruction." Infrared and Laser Engineering 48, no. 1 (2019): 126005. http://dx.doi.org/10.3788/irla201948.0126005.

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13

K. Oyedotun, Oyebade, and Kamil Dimililer. "Pattern Recognition: Invariance Learning in Convolutional Auto Encoder Network." International Journal of Image, Graphics and Signal Processing 8, no. 3 (March 8, 2016): 19–27. http://dx.doi.org/10.5815/ijigsp.2016.03.03.

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14

Lu, Yang, Shujuan Yi, Yurong Liu, and Yuling Ji. "A novel path planning method for biomimetic robot based on deep learning." Assembly Automation 36, no. 2 (April 4, 2016): 186–91. http://dx.doi.org/10.1108/aa-11-2015-108.

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Анотація:

Purpose This paper aims to design a multi-layer convolutional neural network (CNN) to solve biomimetic robot path planning problem. Design/methodology/approach At first, the convolution kernel with different scales can be obtained by using the sparse auto encoder training algorithm; the parameter of the hidden layer is a series of convolutional kernel, and the authors use these kernels to extract first-layer features. Then, the authors get the second-layer features through the max-pooling operators, which improve the invariance of the features. Finally, the authors use fully connected layers of neural networks to accomplish the path planning task. Findings The NAO biomimetic robot respond quickly and correctly to the dynamic environment. The simulation experiments show that the deep neural network outperforms in dynamic and static environment than the conventional method. Originality/value A new method of deep learning based biomimetic robot path planning is proposed. The authors designed a multi-layer CNN which includes max-pooling layer and convolutional kernel. Then, the first and second layers features can be extracted by these kernels. Finally, the authors use the sparse auto encoder training algorithm to train the CNN so as to accomplish the path planning task of NAO robot.

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15

Lou, Shuting, Jiarui Deng, and Shanxiang Lyu. "Chaotic signal denoising based on simplified convolutional denoising auto-encoder." Chaos, Solitons & Fractals 161 (August 2022): 112333. http://dx.doi.org/10.1016/j.chaos.2022.112333.

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16

Wu, Pin, Siquan Gong, Kaikai Pan, Feng Qiu, Weibing Feng, and Christopher Pain. "Reduced order model using convolutional auto-encoder with self-attention." Physics of Fluids 33, no. 7 (July 2021): 077107. http://dx.doi.org/10.1063/5.0051155.

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17

Zhang, Zhihong, Dongdong Chen, Zeli Wang, Heng Li, Lu Bai, and Edwin R. Hancock. "Depth-based subgraph convolutional auto-encoder for network representation learning." Pattern Recognition 90 (June 2019): 363–76. http://dx.doi.org/10.1016/j.patcog.2019.01.045.

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18

Zhou, Yuan, Yeda Zhang, Xukai Xie, and Sun-Yuan Kung. "Image super-resolution based on dense convolutional auto-encoder blocks." Neurocomputing 423 (January 2021): 98–109. http://dx.doi.org/10.1016/j.neucom.2020.09.049.

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19

Qiang, Zhenping, Libo He, Fei Dai, Qinghui Zhang, and Junqiu Li. "Image Inpainting Based on Improved Deep Convolutional Auto-encoder Network." Chinese Journal of Electronics 29, no. 6 (November 1, 2020): 1074–84. http://dx.doi.org/10.1049/cje.2020.09.008.

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20

Subramaniam, Sudha, K. B. Jayanthi, C. Rajasekaran, and C. Sunder. "Measurement of Intima-Media Thickness Depending on Intima Media Complex Segmentation by Deep Neural Networks." Journal of Medical Imaging and Health Informatics 11, no. 10 (October 1, 2021): 2546–57. http://dx.doi.org/10.1166/jmihi.2021.3841.

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Анотація:

Intima Media Thickness (IMT) of the carotid artery is an important marker indicating the sign of cardiovascular disease. Automated measurement of IMT requires segmentation of intima media complex (IMC).Traditional methods which use shape, color and texture for classification have poor generalization capability. This paper proposes two models- the pipeline model and the end-to-end model using Convolutional Neural Networks (CNN) and auto encoder–decoder network respectively. CNN architecture is implemented and tested by varying the number of convolutional layer, size of the kernel as well as the number of kernels. Auto encoder–decoder performs pixel wise classification using two interconnected pathways for identifying the boundary of lumen-intima (LI) and media adventitia (MA). This helps in reconstruction of the segmented portion for measurement of IMT. Both methods are tested using a dataset of 550 subjects. The results clearly indicate that end-to-end model has an edge over the pipeline model exhibiting lesser deviation between the automated measurement and the measurement made by the radiologist. The pipeline model however has better segmentation accuracy when the size of the image used for training is small. The convolutional neural network with auto encoder–decoder proves robust through sparse representation, and faster learning with better generalization. Also, the experimental setup is analyzed by interconnecting Tensor flow simulated result with Raspberry PI and the outcomes are analyzed.

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21

Shi, Han, Haozheng Fan, and James T. Kwok. "Effective Decoding in Graph Auto-Encoder Using Triadic Closure." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 906–13. http://dx.doi.org/10.1609/aaai.v34i01.5437.

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The (variational) graph auto-encoder and its variants have been popularly used for representation learning on graph-structured data. While the encoder is often a powerful graph convolutional network, the decoder reconstructs the graph structure by only considering two nodes at a time, thus ignoring possible interactions among edges. On the other hand, structured prediction, which considers the whole graph simultaneously, is computationally expensive. In this paper, we utilize the well-known triadic closure property which is exhibited in many real-world networks. We propose the triad decoder, which considers and predicts the three edges involved in a local triad together. The triad decoder can be readily used in any graph-based auto-encoder. In particular, we incorporate this to the (variational) graph auto-encoder. Experiments on link prediction, node clustering and graph generation show that the use of triads leads to more accurate prediction, clustering and better preservation of the graph characteristics.

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22

Han, Xiaobing, Yanfei Zhong, and Liangpei Zhang. "SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 25–31. http://dx.doi.org/10.5194/isprsannals-iii-7-25-2016.

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Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets – Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

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23

Han, Xiaobing, Yanfei Zhong, and Liangpei Zhang. "SPATIAL-SPECTRAL CLASSIFICATION BASED ON THE UNSUPERVISED CONVOLUTIONAL SPARSE AUTO-ENCODER FOR HYPERSPECTRAL REMOTE SENSING IMAGERY." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 25–31. http://dx.doi.org/10.5194/isprs-annals-iii-7-25-2016.

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Анотація:

Current hyperspectral remote sensing imagery spatial-spectral classification methods mainly consider concatenating the spectral information vectors and spatial information vectors together. However, the combined spatial-spectral information vectors may cause information loss and concatenation deficiency for the classification task. To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse auto-encoder (UCSAE) with window-in-window selection strategy is proposed in this paper. Window-in-window selection strategy selects the sub-window spatial-spectral information for the spatial-spectral feature learning and extraction with the sparse auto-encoder (SAE). Convolution mechanism is applied after the SAE feature extraction stage with the SAE features upon the larger outer window. The UCSAE algorithm was validated by two common hyperspectral imagery (HSI) datasets – Pavia University dataset and the Kennedy Space Centre (KSC) dataset, which shows an improvement over the traditional hyperspectral spatial-spectral classification methods.

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24

Kollias, Georgios, Vasileios Kalantzis, Tsuyoshi Ide, Aurélie Lozano, and Naoki Abe. "Directed Graph Auto-Encoders." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (June 28, 2022): 7211–19. http://dx.doi.org/10.1609/aaai.v36i7.20682.

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We introduce a new class of auto-encoders for directed graphs, motivated by a direct extension of the Weisfeiler-Leman algorithm to pairs of node labels. The proposed model learns pairs of interpretable latent representations for the nodes of directed graphs, and uses parameterized graph convolutional network (GCN) layers for its encoder and an asymmetric inner product decoder. Parameters in the encoder control the weighting of representations exchanged between neighboring nodes. We demonstrate the ability of the proposed model to learn meaningful latent embeddings and achieve superior performance on the directed link prediction task on several popular network datasets.

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25

Li, Bingyu, Lei Wang, Qiaoyong Jiang, Wei Li, and Rong Huang. "Sports Risk Prediction Model Based on Automatic Encoder and Convolutional Neural Network." Applied Sciences 13, no. 13 (July 4, 2023): 7839. http://dx.doi.org/10.3390/app13137839.

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In view of the limitations of traditional statistical methods in dealing with multifactor and nonlinear data and the inadequacy of classical machine learning algorithms in dealing with and predicting data with high dimensions and large sample sizes, this paper proposes an operational risk prediction model based on an automatic encoder and convolutional neural networks. First, we use an automatic encoder to extract features of motion risk factors and obtain feature components that can highly represent risk. Secondly, based on the causal relationship between sports risk and risk characteristics, a convolutional neural network with a dual convolution layer and dual pooling layer topology is constructed. Finally, the sports risk prediction model is established by combining the auto-coded feature components with the topology of the convolutional neural network. Compared with other algorithms, the proposed method can effectively analyze and extract risk characteristics and has a high prediction accuracy. At the same time, it promotes the integration of sports science and computer science and provides a basis for the application of machine learning in the field of sports risk prediction.

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26

Xingkang, ZHOU, and YU Jianbo. "Gearbox Fault Diagnosis Based on One-dimension Residual Convolutional Auto-encoder." Journal of Mechanical Engineering 56, no. 7 (2020): 96. http://dx.doi.org/10.3901/jme.2020.07.096.

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27

Gunjali, Drakshaveni, and Prasad Naik Hansavath. "Improvised convolutional auto encoder for thyroid nodule image enhancement and segmentation." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (April 1, 2022): 342. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp342-351.

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Анотація:

Thyroid <span>ultrasonography and thermography are a widely used clinical technique for nodule diagnosis in thyroid regions. However, it remains difficult to detect and recognize the nodules due to low contrast, high noise, and diverse appearance of nodules. To alleviate doctors’ tremendous labor in the diagnosis procedure, we advocate a machine learning approach to the detection and recognition tasks in this paper. Moreover, this research mainly focuses on segmenting the image and finding the probable region. In this research work an improvised convolutional auto encoder (ICAE) is introduced for segmenting the image and finding the probable region of thyroid gland and it enhances image. ICAE comprises various layer and mechanism, each having their own task. Apart from the traditional approach, skip connection is applied for the image enhancement and dual frame is introduced for better feature extraction. Further optimization technique is used for increasing the learning rate. ICAE is evaluated considering digital database thyroid image (DDTI) dataset with performance metrics like accuracy, true positive rate, false positive rate, dice coefficient and similarity index (SI); also, comparative analysis is carried out with various existing model and proposed model simply outperforms the existing model.</span>

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28

KISHIMOTO, Takuya, Nobutada FUJII, Ruriko WATANABE, Daisuke KOKURYO, Toshiya KAIHARA, Masahito MANO, and Shinji NISHIGUCHI. "Disease Strain Detection Method of Farm Products Using Convolutional Auto Encoder." Proceedings of Design & Systems Conference 2021.31 (2021): 3406. http://dx.doi.org/10.1299/jsmedsd.2021.31.3406.

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29

Memarzadeh, Milad, Bryan Matthews, and Ilya Avrekh. "Unsupervised Anomaly Detection in Flight Data Using Convolutional Variational Auto-Encoder." Aerospace 7, no. 8 (August 8, 2020): 115. http://dx.doi.org/10.3390/aerospace7080115.

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Анотація:

The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This is in part due the airlines, manufacturers, FAA, and research institutions all continually working to improve the safety of the operations. However, the current approach for identifying vulnerabilities in NAS operations leverages domain expertise using knowledge about how the system should behave within the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to assess risk based on pre-defined safety vulnerabilities. Moreover, state-of-the-art machine learning models that are developed for event detection in aerospace data usually rely on supervised learning. However, in many real-world problems, such as flight safety, creating labels for the data requires specialized expertise that is time consuming and therefore largely impractical. To address this challenge, we develop a Convolutional Variational Auto-Encoder (CVAE), an unsupervised deep generative model for anomaly detection in high-dimensional time-series data. Validating on Yahoo’s benchmark data as well as a case study of identifying anomalies in commercial flights’ take-offs, we show that CVAE outperforms both classic and deep learning-based approaches in precision and recall of detecting anomalies.

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30

Yan, Wenjie, Dong Wang, Mengjing Cao, and Jing Liu. "Deep Auto Encoder Model With Convolutional Text Networks for Video Recommendation." IEEE Access 7 (2019): 40333–46. http://dx.doi.org/10.1109/access.2019.2905534.

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31

Han, Xiaobing, Yanfei Zhong, and Liangpei Zhang. "Spatial-Spectral Unsupervised Convolutional Sparse Auto-Encoder Classifier for Hyperspectral Imagery." Photogrammetric Engineering & Remote Sensing 83, no. 3 (March 1, 2017): 195–206. http://dx.doi.org/10.14358/pers.83.3.195.

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32

Cai, Xi, Suyuan Li, Xinyue Liu, and Guang Han. "Vision-Based Fall Detection With Multi-Task Hourglass Convolutional Auto-Encoder." IEEE Access 8 (2020): 44493–502. http://dx.doi.org/10.1109/access.2020.2978249.

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33

Wang, Jun, Jian Zhou, Liangding Li, Jiapeng Chi, Feiling Yang, and Dezhi Han. "Deep Feature Based on Convolutional Auto-Encoder for Compact Semantic Hashing." Journal of Physics: Conference Series 1229 (May 2019): 012032. http://dx.doi.org/10.1088/1742-6596/1229/1/012032.

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34

Nishio, Mizuho, Chihiro Nagashima, Saori Hirabayashi, Akinori Ohnishi, Kaori Sasaki, Tomoyuki Sagawa, Masayuki Hamada, and Tatsuo Yamashita. "Convolutional auto-encoder for image denoising of ultra-low-dose CT." Heliyon 3, no. 8 (August 2017): e00393. http://dx.doi.org/10.1016/j.heliyon.2017.e00393.

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35

Wang, Fei, Qiming Ma, Wenhan Liu, Sheng Chang, Hao Wang, Jin He, and Qijun Huang. "A novel ECG signal compression method using spindle convolutional auto-encoder." Computer Methods and Programs in Biomedicine 175 (July 2019): 139–50. http://dx.doi.org/10.1016/j.cmpb.2019.03.019.

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36

Zhang, Han, Jiadong Hua, Tong Tong, Tian Zhang, and Jing Lin. "Dispersion compensation of Lamb waves based on a convolutional auto-encoder." Mechanical Systems and Signal Processing 198 (September 2023): 110432. http://dx.doi.org/10.1016/j.ymssp.2023.110432.

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37

Lu, Yingjing. "The Level Weighted Structural Similarity Loss: A Step Away from MSE." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9989–90. http://dx.doi.org/10.1609/aaai.v33i01.33019989.

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The Mean Square Error (MSE) has shown its strength when applied in deep generative models such as Auto-Encoders to model reconstruction loss. However, in image domain especially, the limitation of MSE is obvious: it assumes pixel independence and ignores spatial relationships of samples. This contradicts most architectures of Auto-Encoders which use convolutional layers to extract spatial dependent features. We base on the structural similarity metric (SSIM) and propose a novel level weighted structural similarity (LWSSIM) loss for convolutional Auto-Encoders. Experiments on common datasets on various Auto-Encoder variants show that our loss is able to outperform the MSE loss and the Vanilla SSIM loss. We also provide reasons why our model is able to succeed in cases where the standard SSIM loss fails.

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38

Hao, Cui, Wang Kesheng, Li Yu, Yang Binyuan, and miao Qiang. "A data enlargement strategy for fault classification through a convolutional auto-encoder." MATEC Web of Conferences 255 (2019): 05001. http://dx.doi.org/10.1051/matecconf/201925505001.

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The amount of data is of crucial to the accuracy of fault classification through machine learning techniques. In wind energy harvest industry, due to the shortage of faulty data obtained in real practice, together with ever changing operational conditions, fault detection and evaluation of wind turbine blade problems become intractable through conventional machine learning methods. In this paper, a modified unsupervised learning method, namely a convolutional auto-encoder based data enlargement strategy (ABE) is proposed for wind turbine blade fault classification. Limited simulation results for different levels of wind turbine icy blades are used for investigation. First, convolutional auto encoder is used to increase the amount of the data. Then, decision tree based xgboost tool, as an example, is used to demonstrate the effectiveness of data enlargement strategy for fault classification. The study shows that the proposed data enlargement strategy is an effective method to improve the fault classification accuracy through machine learning techniques.

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39

Tsui, Benjamin, William A. P. Smith, and Gavin Kearney. "Low-Order Spherical Harmonic HRTF Restoration Using a Neural Network Approach." Applied Sciences 10, no. 17 (August 20, 2020): 5764. http://dx.doi.org/10.3390/app10175764.

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Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse head related transfer function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions into high frequency representations of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of convolutional auto-encoder (CAE) and denoising auto-encoder (DAE) models is proposed to restore the high frequency distortion in SH-interpolated HRTFs. Results were evaluated using both perceptual spectral difference (PSD) and localisation prediction models, both of which demonstrated significant improvement after the restoration process.

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40

Gong, Xuejiao, Bo Tang, Ruijin Zhu, Wenlong Liao, and Like Song. "Data Augmentation for Electricity Theft Detection Using Conditional Variational Auto-Encoder." Energies 13, no. 17 (August 19, 2020): 4291. http://dx.doi.org/10.3390/en13174291.

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Due to the strong concealment of electricity theft and the limitation of inspection resources, the number of power theft samples mastered by the power department is insufficient, which limits the accuracy of power theft detection. Therefore, a data augmentation method for electricity theft detection based on the conditional variational auto-encoder (CVAE) is proposed. Firstly, the stealing power curves are mapped into low dimensional latent variables by using the encoder composed of convolutional layers, and the new stealing power curves are reconstructed by the decoder composed of deconvolutional layers. Then, five typical attack models are proposed, and the convolutional neural network is constructed as a classifier according to the data characteristics of stealing power curves. Finally, the effectiveness and adaptability of the proposed method is verified by a smart meters’ data set from London. The simulation results show that the CVAE can take into account the shapes and distribution characteristics of samples at the same time, and the generated stealing power curves have the best effect on the performance improvement of the classifier than the traditional augmentation methods such as the random oversampling method, synthetic minority over-sampling technique, and conditional generative adversarial network. Moreover, it is suitable for different classifiers.

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41

LI Ming, 李明, 刘帆 LIU Fan та 李婧芝 LI Jingzhi. "结合卷积注意模块与卷积自编码器的细节注入遥感图像融合". ACTA PHOTONICA SINICA 51, № 6 (2022): 0610005. http://dx.doi.org/10.3788/gzxb20225106.0610005.

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42

Ayaluri, Mallikarjuna Reddy, Sudheer Reddy K., Srinivasa Reddy Konda, and Sudharshan Reddy Chidirala. "Efficient steganalysis using convolutional auto encoder network to ensure original image quality." PeerJ Computer Science 7 (February 16, 2021): e356. http://dx.doi.org/10.7717/peerj-cs.356.

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Steganalysis is the process of analyzing and predicting the presence of hidden information in images. Steganalysis would be most useful to predict whether the received images contain useful information. However, it is more difficult to predict the hidden information in images which is computationally difficult. In the existing research method, this is resolved by introducing the deep learning approach which attempts to perform steganalysis tasks in effectively. However, this research method does not concentrate the noises present in the images. It might increase the computational overhead where the error cost adjustment would require more iteration. This is resolved in the proposed research technique by introducing the novel research method called Non-Gaussian Noise Aware Auto Encoder Convolutional Neural Network (NGN-AEDNN). Classification technique provides a more flexible way for steganalysis where the multiple features present in the environment would lead to an inaccurate prediction rate. Here, learning accuracy is improved by introducing noise removal techniques before performing a learning task. Non-Gaussian Noise Removal technique is utilized to remove the noises before learning. Also, Gaussian noise removal is applied at every iteration of the neural network to adjust the error rate without the involvement of noisy features. This proposed work can ensure efficient steganalysis by accurate learning task. Matlab has been employed to implement the method by performing simulations from which it is proved that the proposed research technique NGN-AEDNN can ensure the efficient steganalysis outcome with the reduced computational overhead when compared with the existing methods.

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43

Lee, Chang-Hun, Sang-Kwon Lee, and Pung-Il Kim. "Fault Detection and Diagnosis of Chain Transmission System Using Convolutional Auto-encoder." Transactions of the Korean Society for Noise and Vibration Engineering 31, no. 5 (October 20, 2021): 563–73. http://dx.doi.org/10.5050/ksnve.2021.31.5.563.

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44

Prabu, P., R. Sivakumar, and B. Ramamurthy. "Corpus based sentimenal movie review analysis using auto encoder convolutional neural network." Journal of Discrete Mathematical Sciences and Cryptography 24, no. 8 (November 17, 2021): 2323–39. http://dx.doi.org/10.1080/09720529.2021.2014139.

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45

Liu, Jie, Qiu Tang, Wei Qiu, Jun Ma, Yuhong Qin, and Biao Sun. "Automatic Power Quality Disturbance Diagnosis Based on Residual Denoising Convolutional Auto-Encoder." Applied Sciences 11, no. 16 (August 20, 2021): 7637. http://dx.doi.org/10.3390/app11167637.

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With the increasing integration of non-linear electronic loads, the diagnosis and classification of power quality are becoming crucial for power grid signal management. This paper presents a novel diagnosis strategy based on unsupervised learning, namely residual denoising convolutional auto-encoder (RDCA), which extracts features from the complex power quality disturbances (PQDs) automatically. Firstly, the time–frequency analysis is applied to isolate frequency domain information. Then, the RDCA with a weight residual structure is utilized to extract the useful features in the contaminated PQD data, where the performance is improved using the residual structure. A single-layer convolutional neural network (SCNN) with an added batch normalization layer is proposed to classify the features. Furthermore, combining with RDCA and SCNN, we further propose a classification framework to classify complex PQDs. To provide a reasonable interpretation of the RDCA, visual analysis is employed to gain insight into the model, leading to a better understanding of the features from different layers. The simulation and experimental tests are conducted to verify the practicability and robustness of the RDCA.

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46

Kalphana, I., and T. Kesavamurthy. "Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System." Computer Systems Science and Engineering 41, no. 1 (2022): 171–85. http://dx.doi.org/10.32604/csse.2022.019799.

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47

Zhong, Wei, Xuemei Guo, and Guoli Wang. "Maternal ECG removal using short time Fourier transform and convolutional auto-encoder." International Journal of Data Mining and Bioinformatics 23, no. 2 (2020): 160. http://dx.doi.org/10.1504/ijdmb.2020.10029556.

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48

Zhong, Wei, Xuemei Guo, and Guoli Wang. "Maternal ECG removal using short time Fourier transform and convolutional auto-encoder." International Journal of Data Mining and Bioinformatics 23, no. 2 (2020): 160. http://dx.doi.org/10.1504/ijdmb.2020.107381.

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49

Zhang, Yi-yang, Yong Feng, Da-jiang Liu, Jia-xing Shang, and Bao-hua Qiang. "FRWCAE: joint faster-RCNN and Wasserstein convolutional auto-encoder for instance retrieval." Applied Intelligence 50, no. 7 (March 2, 2020): 2208–21. http://dx.doi.org/10.1007/s10489-019-01625-y.

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50

Liu, Beibei, Weiping Hu, and Fan Li. "Improve the spot-like coding detection of U-net auto-encoder." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012095. http://dx.doi.org/10.1088/1742-6596/2216/1/012095.

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Abstract The dot-like spray code on the product packaging has always been a difficult problem in industrial inspection due to its complicated background in the printing area, diverse characters in the code, and changeable fonts. With the powerful capabilities of deep learning in the field of computer vision, related algorithms have become popular solutions in the field of computer vision in recent years. The method based on convolutional autoencoders to achieve inkjet detection on food packaging boxes has become a feasible solution. This method takes the mask of the coding character area on the food packaging box as the final goal of network learning. The network inputs the picture with the coding area, and reconstructs the original background of the coding area. At the same time the network uses the residual channel attention mechanism to restore the details of the image, and at the same time introduces the gaussian operator to calculate the loss of network reconstruction. Since the convolutional auto-encoder is an unsupervised learning method, the training data does not require a large amount of manual labeling, and a good solution is proposed for scenarios such as small data sets in industrial production and difficulty in labeling. Through real-time testing of the coding data of the pipeline, it is verified that the method can effectively detect the coding area.

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