Relevant bibliographies by topics / Convolutional Auto-Encoder

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Convolutional Auto-Encoder'

Author: Grafiati
Published: 6 September 2023
Last updated: 25 July 2025

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Journal articles on the topic "Convolutional Auto-Encoder"

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

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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-e
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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 (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-dimens
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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 (2019): 352–58. http://dx.doi.org/10.5391/jkiis.2019.29.5.352.

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

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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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AIRCC. "AUTO ENCODER CONVOLUTIONAL NEURAL NETWORK FOR PNEUMONIA DETECTION." International Journal of Artificial Intelligence & Applications (IJAIA) 15, no. 5 (2024): 21–32. https://doi.org/10.5121/ijaia.2024.15502.

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This study presents an innovative approach utilising Autoencoder Convolutional Neural Networks (AECNNs) for pneumonia detection in paediatric chest x-rays. The research addresses the complexity ofpneumonia, considering diverse causative agents, including bacteria, viruses, and aspiration. AutoencoderConvolutional Neural Networks are employed to enhance anomaly detection by revealing hidden patternsin the data. The evaluation process involves meticulous analysis of the histogram reconstruction error,leading to the establishment of a threshold for anomaly identification. The results demonstrate
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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 (2019): 124–36. http://dx.doi.org/10.5373/jardcs/v11i12/20193221.

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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 (2021): 1–21. http://dx.doi.org/10.1145/3434767.

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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 convolut
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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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Dissertations / Theses on the topic "Convolutional Auto-Encoder"

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Ionascu, Beatrice. "Modelling user interaction at scale with deep generative methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239333.

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Understanding how users interact with a company's service is essential for data-driven businesses that want to better cater to their users and improve their offering. By using a generative machine learning approach it is possible to model user behaviour and generate new data to simulate or recognize and explain typical usage patterns. In this work we introduce an approach for modelling users' interaction behaviour at scale in a client-service model. We propose a novel representation of multivariate time-series data as time pictures that express temporal correlations through spatial organizatio
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Sayadi, Karim. "Classification du texte numérique et numérisé. Approche fondée sur les algorithmes d'apprentissage automatique." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066079/document.

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Différentes disciplines des sciences humaines telles la philologie ou la paléographie font face à des tâches complexes et fastidieuses pour l'examen des sources de données. La proposition d'approches computationnelles en humanités permet d'adresser les problématiques rencontrées telles que la lecture, l'analyse et l'archivage de façon systématique. Les modèles conceptuels élaborés reposent sur des algorithmes et ces derniers donnent lieu à des implémentations informatiques qui automatisent ces tâches fastidieuses. La première partie de la thèse vise, d'une part, à établir la structuration thém
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Chen, Peng-Cheng, and 陳鵬丞. "Semi-Supervised Learning Framework with an Auto-Elastic Convolutional Auto-Encoder for Image Classification Design." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/sv6v8s.

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碩士<br>逢甲大學<br>自動控制工程學系<br>106<br>More and more attention of the deep learning for image and speech recognition, even artificial intelligence (AI) beats humans in ancient game of Go has been attracted with the development of computer and electronic devices. In neural networks, deep learning with convolutional neural network (CNN) has been widely utilized in numerous applications of automatic image recognition, such as multiple object detection and classification, object tracking, sematic segmentation, and most of them have achieved outstanding results. However, with the applications are becomin
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Liao, Chi-Jou, and 廖綺柔. "Using Convolutional Neural Network Auto-encoder in Breast Tumors Classification and Detection Compare with Traditional Ultrasound BIRADS." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/tfz345.

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Paul, Subir. "Hyperspectral Remote Sensing for Land Cover Classification and Chlorophyll Content Estimation using Advanced Machine Learning Techniques." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4537.

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In the recent years, remote sensing data or images have great potential for continuous spatial and temporal monitoring of Earth surface features. In case of optical remote sensing, hyperspectral (HS) data contains abundant spectral information and these information are advantageous for various applications. However, high-dimensional HS data handling is a very challenging task. Different techniques are proposed as a part of this thesis to handle the HS data in a computationally efficient manner and to achieve better performance for land cover classification and chlorophyll content prediction. P
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Book chapters on the topic "Convolutional Auto-Encoder"

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Chen, Wei, Ruimin Hu, Xiaochen Wang, and Dengshi Li. "HRTF Representation with Convolutional Auto-encoder." In MultiMedia Modeling. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37731-1_49.

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Wang, Taizheng, Chunyang Ye, Hui Zhou, Mingwang Ou, and Bo Cheng. "AIS Ship Trajectory Clustering Based on Convolutional Auto-encoder." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55187-2_39.

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Wang, Diangang, Wei Gan, Chenyang Yan, Kun Huang, and Hongyi Wu. "Inception Model of Convolutional Auto-encoder for Image Denoising." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64214-3_12.

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Shamsi, Meysam, Damien Lolive, Nelly Barbot, and Jonathan Chevelu. "Script Selection Using Convolutional Auto-encoder for TTS Speech Corpus." In Speech and Computer. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26061-3_43.

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Xu, Chaoyang, Ling Wu, and Shiping Wang. "Unsupervised Dimension Reduction for Image Classification Using Regularized Convolutional Auto-Encoder." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17795-9_8.

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Han, Xiaobing, Yanfei Zhong, Lifang He, Philip S. Yu, and Liangpei Zhang. "The Unsupervised Hierarchical Convolutional Sparse Auto-Encoder for Neuroimaging Data Classification." In Brain Informatics and Health. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23344-4_16.

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Arslan, Abdullah Taha, and Ugur Yayan. "Convolutional Auto-Encoder Based Degradation Point Forecasting for Bearing Data Set." In Artificial Intelligence and Applied Mathematics in Engineering Problems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36178-5_71.

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Xiang, Xinyu, Ping Zhang, Qiang Yuan, Renping Li, Runqiao Hu, and Ke Li. "Few-Shot Learning Based on Convolutional Denoising Auto-encoder Relational Network." In Communications in Computer and Information Science. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9247-5_8.

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Li, Chun, Wenfeng Shi, and Lin Shang. "Latent Feature Representation for Cohesive Community Detection Based on Convolutional Auto-Encoder." In Big Data. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1899-7_27.

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Shuvo, M. I. R., M. A. H. Akhand, and N. Siddique. "Handwritten Numeral Superposition to Printed Form Using Convolutional Auto-Encoder and Recognition Using Convolutional Neural Network." In Proceedings of International Joint Conference on Computational Intelligence. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3607-6_14.

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Conference papers on the topic "Convolutional Auto-Encoder"

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Dao-Xuan, Son, Khoi Nghiem-Tuan, Duong Truong-Dai, Anh Ong-Tung, Chi Hoang-Phuong, and Minh Nguyen-Duc. "Implementing Convolutional Auto Encoder on FPGA using High Level Synthesis." In 2024 Tenth International Conference on Communications and Electronics (ICCE). IEEE, 2024. http://dx.doi.org/10.1109/icce62051.2024.10634692.

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Srivastava, Yashi, Ruchira Rawat, Anurag Shrivastava, et al. "Apple Fruit Flower Detection Using Auto-Encoder Convolutional Neural Network." In 2024 1st International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI). IEEE, 2024. https://doi.org/10.1109/icscai61790.2024.10866483.

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Tao, Shuailin, Jinhai Hu, Wang Ling Goh, and Yuan Gao. "Convolutional Auto-Encoder for Variable Length Respiratory Sound Compression and Reconstruction." In 2024 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2024. https://doi.org/10.1109/biocas61083.2024.10798320.

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Dalila, Chikhaoui, Beladgham Mohammed, and Ben Aissa Mohamed. "Region-Based Retinal Image Compression Using Convolutional Auto-Encoder And Arithmetic Encoding." In 2024 International Conference on Advances in Electrical and Communication Technologies (ICAECOT). IEEE, 2024. https://doi.org/10.1109/icaecot62402.2024.10829238.

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Alsalami, Zaid, E. G. Satish, M. PonniBala, J. Rajalakshmi, and C. Sushama. "A Denoising Convolutional Auto Encoder with Bi-LSTM for Corrupted ECG Signals Reduction." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691263.

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Vijay Kumar, Bura, Saif O. Husain, Megha Kulkarni, Abhijeet Das, and Sasikumar Gurumoorthy. "LULC Classification Based on Multi-Scale Convolutional Auto Encoder with Artificial Bee Colony." In 2024 International Conference on Data Science and Network Security (ICDSNS). IEEE, 2024. http://dx.doi.org/10.1109/icdsns62112.2024.10691268.

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Geethaprathibha, K., Haider Alabdeli, Mamatha Bai B G, Bura Vijay Kumar, and Boya Divya. "Urban Change Detection based Convolutional Auto Encoder - Content Loss using High-Resolution Images." In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC). IEEE, 2024. https://doi.org/10.1109/icdscnc62492.2024.10940045.

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Nadafi, Ahmad Reza, and Farahnaz Mohanna. "Automatic Identification and Reconstruction of Tuberculosis in Microscopic Images Using Convolutional Auto-Encoder Network." In 2024 15th International Conference on Information and Knowledge Technology (IKT). IEEE, 2024. https://doi.org/10.1109/ikt65497.2024.10892615.

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Yang, Fan, Huifang Chen, and Lei Xie. "Convolutional Auto-encoder-Based Packet-Loss-and-Error-Resilient Data Collection Method for Underwater Sensors." In 2024 13th International Conference on Communications, Circuits and Systems (ICCCAS). IEEE, 2024. http://dx.doi.org/10.1109/icccas62034.2024.10652661.

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Peng, Yongxin. "An End-to-end Framework for Graph Clustering: Semantic Fusion of Graph Convolutional Network and Variational Auto-encoder." In 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2024. https://doi.org/10.1109/icicml63543.2024.10957874.

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