Suchergebnisse - sparse convolutional autoencoder (((same OR sage) OR cae))

A Novel Convolutional Autoencoder-Based Clutter Removal Method for Buried Threat Detection in Ground-Penetrating Radar von Temlioglu, Eyyup, Erer, Isin

“… A new clutter removal method based on convolutional autoencoders (CAEs) is introduced. The raw GPR image is encoded via successive convolution and pooling layers and then decoded to provide the clutter-free GPR image …”
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Unsupervised Spatial-Spectral Feature Learning by 3D Convolutional Autoencoder for Hyperspectral Classification von Mei, Shaohui, Ji, Jingyu, Geng, Yunhao, Zhang, Zhi, Li, Xu, Du, Qian

“… ) convolutional autoencoder (3D-CAE). The proposed 3D-CAE consists of 3D or elementwise operations only, such as 3D convolution, 3D pooling, and 3D batch normalization, to maximally explore spatial-spectral structure information for feature extraction …”
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Improving brain MRI denoising using convolutional AutoEncoder and sparse representations von Velayudham, A, Madhan Kumar, K., Krishna Priya, MS

“… However, noise often degrades image quality, leading to inaccurate diagnoses. To address this issue, a Convolutional AutoEncoder-based Orthogonal Matching Pursuit (CAE-OMP …”
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Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images von Hou, Le, Nguyen, Vu, Kanevsky, Ariel B., Samaras, Dimitris, Kurc, Tahsin M., Zhao, Tianhao, Gupta, Rajarsi R., Gao, Yi, Chen, Wenjin, Foran, David, Saltz, Joel H.

“… We propose a sparse Convolutional Autoencoder (CAE) for simultaneous nucleus detection and feature extraction in histopathology tissue images …”
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Multi-resolution reconstruction of longitudinal streambed footprints using embedded sparse convolutional autoencoders von Yang, Yifan, Tang, Zihao, Shao, Dong, Xu, Zhonghou

“… This study introduces an embedded convolutional autoencoder (CAE) architecture designed for the multi-resolution reconstruction of longitudinal streambed footprints as sparse heatmaps …”
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Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM von Binbusayyis, Adel, Vaiyapuri, Thavavel

“… With the rapid advancement in network technologies, the need for cybersecurity has gained increasing momentum in recent years. As a primary defense mechanism, …”
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Enhanced fault detection in digital VLSI circuits using convolutional autoencoders von Savalam, Chandrasekhar, Medisetti, Sanjay, Korapati, Prasanti

“… A Convolutional Autoencoder (CAE) is employed to extract spatial and structural features from circuit test patterns, effectively reducing dimensionality while preserving fault-related information …”
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Leveraging variant of CAE with sparse convolutional embedding and two-stage application-driven data augmentation for image clustering von Liu, Yanming, Liu, Jinglei

“… To achieve this, we propose a variant of the convolutional autoencoder (CAE) called SCDAC, which incorporates sparse convolutional embedding and a two-stage application-driven data augmentation approach …”
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Temperature scaling unmixing framework based on convolutional autoencoder von Xu, Jin, Xu, Mingming, Liu, Shanwei, Sheng, Hui, Yang, Zhiru

“… •The framework is a new spatial level constraint method and can be transferred to other convolutional autoencoder-based methods …”
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Deep Learning Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders von Wang, Yueya, Shi, Xiaoming, Lei, Lili, Fung, Jimmy Chi-Hung

“… By training a convolutional autoencoder (CAE) with a long simulation at a coarse “forecast” resolution (T63), we obtained a deep learning approximation …”
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Representation learning via an integrated autoencoder for unsupervised domain adaptation von ZHU, Yi, WU, Xindong, QIANG, Jipeng, YUAN, Yunhao, LI, Yun

“… Recently, deep learning methods based on autoencoder have achieved sound performance in representation learning, and many dual or serial autoencoder-based methods take different characteristics …”
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Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder von Zhao, Yu, Dong, Qinglin, Chen, Hanbo, Iraji, Armin, Li, Yujie, Makkie, Milad, Kou, Zhifeng, Liu, Tianming

“… •A new deep 3D convolutional autoencoder to model brain network maps.•Derived fine-granularity functional brain network atlases …”
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Feature extraction of fields of fluid dynamics data using sparse convolutional autoencoder von Obayashi, Wataru, Aono, Hikaru, Tatsukawa, Tomoaki, Fujii, Kozo

“… The technique here is based on the convolutional and sparse autoencoder learning algorithms and is called sparse convolutional autoencoder …”
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Deep hyperspectral clustering using attention-enhanced 3D-2D convolutional autoencoder for mineral mapping von Peyghambari, Sima, Zhang, Yun

“… However, the most commonly used 3D-convolutional autoencoder (3D-CAE) models have several disadvantages, including intensive computational costs and the potential to lose spatial information …”
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Deep spectral unmixing framework via 3D denoising convolutional autoencoder von Jia, Peiyuan, Zhang, Miao, Shen, Yi

“… ‐based framework for unmixing problem. It contains two parts: a three‐dimensional convolutional autoencoder for hyperspectral denoising (denoising 3D CAE …”
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Intelligent Fault Diagnosis Method for Blade Damage of Quad-Rotor UAV Based on Stacked Pruning Sparse Denoising Autoencoder and Convolutional Neural Network von Yang, Pu, Wen, Chenwan, Geng, Huilin, Liu, Peng

“… This paper introduces a new intelligent fault diagnosis method based on stack pruning sparse denoising autoencoder and convolutional neural network (sPSDAE-CNN …”
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Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models von Melo, Renato, Finotti, Rafaelle, Guedes, António, Gonçalves, Vítor, Meixedo, Andreia, Ribeiro, Diogo, Barbosa, Flávio, Cury, Alexandre

“… ), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization …”
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Structural Damage Identification Using Autoencoders: A Comparative Study von Spínola Neto, Marcos, Finotti, Rafaelle, Barbosa, Flávio, Cury, Alexandre

“… Autoencoders, as unsupervised learning models, offer promise for SHM by learning data features and reducing dimensionality …”
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Deep learning for pixel-level image fusion: Recent advances and future prospects von Liu, Yu, Chen, Xun, Wang, Zengfu, Wang, Z. Jane, Ward, Rabab K., Wang, Xuesong

“… By integrating the information contained in multiple images of the same scene into one composite image, pixel-level image fusion is recognized as having high significance in a variety of fields …”
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The effect of whitening transformation on pooling operations in convolutional autoencoders von Li, Zuhe, Fan, Yangyu, Liu, Weihua

“… Convolutional autoencoders (CAEs) are unsupervised feature extractors for high-resolution images …”
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