Relevant bibliographies by topics / Convolutional Auto-Encoder

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Academic literature on the topic 'Convolutional Auto-Encoder'

Author: Grafiati
Published: 6 September 2023
Last updated: 15 June 2025

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Journal articles on the topic "Convolutional Auto-Encoder"

1

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-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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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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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-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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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 (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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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 (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 (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 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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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 (2019): 8–13. http://dx.doi.org/10.7746/jkros.2019.14.1.008.

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