Search Results - sparse convolutional autoencoder ((((sace OR sae) OR sage) OR cae))

A Novel Convolutional Autoencoder-Based Clutter Removal Method for Buried Threat Detection in Ground-Penetrating Radar by 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 by 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 by 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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EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder by Liu, Junxiu, Wu, Guopei, Luo, Yuling, Qiu, Senhui, Yang, Su, Li, Wei, Bi, Yifei

“…), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding…”
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Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images by 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 by 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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Static video summarization using multi-CNN with sparse autoencoder and random forest classifier by Nair, Madhu S., Mohan, Jesna

“…). The features are extracted using four pre-trained models of CNN. These vectors are fed to Sparse Autoencoder, which outputs a combined representation of the input feature vectors…”
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SV-SAE: Layer-Wise Pruning for Autoencoder Based on Link Contributions by Rheey, Joohong, Park, Hyunggon

“… The resulting pruned model is referred to as a Shapley Value-based Sparse AutoEncoder (SV-SAE). Using cooperative game theory, the proposed algorithm models the autoencoder…”
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Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM by 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 by 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 by 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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Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models by 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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Transfer learning based on improved stacked autoencoder for bearing fault diagnosis by Luo, Shuyang, Huang, Xufeng, Wang, Yanzhi, Luo, Rongmin, Zhou, Qi

“… Stacked autoencoder (SAE) has been widely employed in deep transfer learning research since it is a semi-supervised algorithm…”
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Temperature scaling unmixing framework based on convolutional autoencoder by 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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Structural Damage Identification Using Autoencoders: A Comparative Study by 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 Augmented Data Assimilation: Reconstructing Missing Information with Convolutional Autoencoders by 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 by 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 by 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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Deep learning for pixel-level image fusion: Recent advances and future prospects by Liu, Yu, Chen, Xun, Wang, Zengfu, Wang, Z. Jane, Ward, Rabab K., Wang, Xuesong

“…•The difficulties that exist in conventional image fusion research are analyzed.•The advantages of deep learning (DL) techniques for image fusion are…”
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Microscopic segmentation and classification of COVID‐19 infection with ensemble convolutional neural network by Amin, Javeria, Anjum, Muhammad Almas, Sharif, Muhammad, Rehman, Amjad, Saba, Tanzila, Zahra, Rida

“… In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE…”
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