Search Results - sparse convolutional autoencoder (cae)

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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Investigating Beta-Variational Convolutional Autoencoders for the Unsupervised Classification of Chest Pneumonia by Akila, Serag Mohamed, Imanov, Elbrus, Almezhghwi, Khaled

“…The world’s population is increasing and so is the challenge on existing healthcare infrastructure to cope with the growing demand in medical diagnosis and…”
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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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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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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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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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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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Industrial Internet of Things Cyber Threats Detection Through Deep Feature Learning and Stacked Sparse Autoencoder Based Classification by Vijay Anand, R., Magesh, G., Alagiri, I., Brahmam, Madala Guru, Senthil Kumar, C., Kesavan, M., Abdullah, Azween Bin

“…ABSTRACT In recent times, the industrial system has integrated with industrial Internet of Things (IoT) applications to enable the ease of production process…”
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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 Convolutional Autoencoder for Energy-efficient Smart Health Wearables In the Era of Big Data by Wong, Junhua, Zhang, Qingxue

“… We leverage deep learning, more specifically, Convolutional Autoencoder (CAE), to learn a sparse representation of the vital big data, thereby minimizing the power consumption of the communication module - the most power-hungry module in the wearable…”
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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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Patch-based Sparse and Convolutional Autoencoders for Anomaly Detection in Hyperspectral Images by Rezvanian, Amir Reza, Imani, Maryam, Ghassemian, Hassan

“… The proposed networks are deep fully-connected sparse autoencoders (SAE) and deep one-dimensional convolutional autoencoders (CAE…”
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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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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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A Novel Harris Hawk Algorithm (HHA) to Optimize Deep Autoencoder (DAE) Method of Deep Learning for Cancer Diagnosis by Kumar, N. Jagadish, Yuvaraj, G., Hameed, A. Thariq, Noor, A. Mohamed Sheik, Johindhan, E., Vasanth, P. Rudresh

“… A deep autoencoder architecture…”
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Deep hyperspectral clustering using attention-enhanced 3D-2D convolutional autoencoder for mineral mapping by 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 by 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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