Suchergebnisse - 3D convolutional autoencoder*

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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Spatial–Spectral Joint Hyperspectral Anomaly Detection Based on a Two-Branch 3D Convolutional Autoencoder and Spatial Filtering von Lv, Shuai, Zhao, Siwei, Li, Dandan, Pang, Boyu, Lian, Xiaoying, Liu, Yinnian

“… –spectral joint HAD method based on a two-branch 3D convolutional autoencoder and spatial filtering …”
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Data‐Driven Detection of Internal Erosion Initiation in Gap‐Graded Soils: Combining Particle‐Scale CFD‐DEM Simulation With 3D Convolutional Autoencoder von Qi, Jie, Yousefpour, Negin, Narsilio, Guillermo A., Pouragha, Mehdi

“… –fluid interactions, which are used to train deep learning models. Autoencoder models with 3D convolutional neural network (CNN …”
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Corrections to "Spatially Aware Fusion in 3D Convolutional Autoencoders for Video Anomaly Detection" von Niaz, Asim, Ul Amin, Sareer, Soomro, Shafiullah, Zia, Hamza, Choi, Kwang Nam

“… Presents corrections to the paper, (Corrections to "Spatially Aware Fusion in 3D Convolutional Autoencoders for Video Anomaly Detection") …”
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Spatial feature fusion in 3D convolutional autoencoders for lung tumor segmentation from 3D CT images von Najeeb, Suhail, Bhuiyan, Mohammed Imamul Hassan

“… In this paper, we introduce a novel approach which makes use of the spatial features learned at different levels of a 2D convolutional autoencoder to create a 3D segmentation network capable …”
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Deep learning-based automated 3D inspection of helical gears using voxelized CAD models and 3D convolutional autoencoders von Selloum, Rabia, Ameddah, Hacene, Brioua, Mourad

“… We propose a voxel-based 3D inspection framework that integrates an XGBoost-guided perturbation model with a 3D convolutional autoencoder (3D CNN-AE …”
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A 3D-convolutional-autoencoder embedded Siamese-attention-network for classification of hyperspectral images von Ranjan, Pallavi, Kumar, Rajeev, Girdhar, Ashish

“… The classification of hyperspectral images (HSI) into categories that correlate to various land cover sorts such as water bodies, agriculture and urban areas, …”
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Detecting spatiotemporal irregularities in videos via a 3D convolutional autoencoder von Yan, Mengjia, Meng, Jingjing, Zhou, Chunluan, Tu, Zhigang, Tan, Yap-Peng, Yuan, Junsong

“… To this end, we introduce a 3D fully convolutional autoencoder (3D-FCAE) that is trainable in an end-to-end manner to detect both temporal and spatiotemporal irregularities in videos using limited training data …”
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An Attention-Based 3D Convolutional Autoencoder for Few-Shot Hyperspectral Unmixing and Classification von Li, Chunyu, Cai, Rong, Yu, Junchuan

“… A cube-based attention 3D convolutional autoencoder network (CACAE), is applied to extract spectral–spatial features …”
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Research on 3D convolutional autoencoder enhanced metro abnormal behavior detection based on multi-level attentional memory von Ye, Run, Zhang, Kun, Zhang, Cheng, Yan, Bin, Zhou, Xiaojia

“… Subway is one of the most important rail transit tools in China, which has the advantages of convenience, safety and high efficiency, and subway has also …”
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Unsupervised Satellite Image Time Series Clustering Using Object-Based Approaches and 3D Convolutional Autoencoder von Kalinicheva, Ekaterina, Sublime, Jérémie, Trocan, Maria

“… It is achieved by using a compressed SITS representation obtained with a multi-view 3D convolutional autoencoder …”
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Spatially Aware Fusion in 3D Convolutional Autoencoders for Video Anomaly Detection von Niaz, Asim, Ul Amin, Sareer, Soomro, Shafiullah, Zia, Hamza, Nam Choi, Kwang

“… The model employs a three-dimensional (3D) convolutional autoencoder, with an encoder-decoder structure that learns spatiotemporal representations and reconstructs the input through the latent space …”
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Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation von Mack, Julian, Arcucci, Rossella, Molina-Solana, Miguel, Guo, Yi-Ke

“… We propose a new ‘Bi-Reduced Space’ approach to solving 3D Variational Data Assimilation using Convolutional Autoencoders …”
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Deep clustering using 3D attention convolutional autoencoder for hyperspectral image analysis von Zheng, Ziyou, Zhang, Shuzhen, Song, Hailong, Yan, Qi

“… ) with t- distributed stochastic neighbor embedding (t-SNE). The reduced dataset is then input into a three-dimensional attention convolutional autoencoder (3D-ACAE …”
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The Use of 3D Convolutional Autoencoder in Fault and Fracture Network Characterization von Xu, Feng, Li, Zhiyong, Wen, Bo, Huang, Youhui, Wang, Yaojun

“… In this paper, a fault and fracture network characterization method based on 3D convolutional autoencoder is proposed …”
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Learning Unsupervised Visual Representations using 3D Convolutional Autoencoder with Temporal Contrastive Modeling for Video Retrieval von Kumar, Vidit, Tripathi, Vikas, Pant, Bhaskar

“… However, this paper designs and studies a 3D convolutional autoencoder …”
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3D-DCDAE: Unsupervised Music Latent Representations Learning Method Based on a Deep 3D Convolutional Denoising Autoencoder for Music Genre Classification von Qiu, Lvyang, Li, Shuyu, Sung, Yunsick

“… This paper proposes an unsupervised latent music representation learning method based on a deep 3D convolutional denoising autoencoder (3D-DCDAE …”
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Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders von Sultanov, Seyfal, Ayyubi, R. A. W., Buban, James P., Klie, Robert F.

“… A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy …”
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3D convolutional selective autoencoder for instability detection in combustion systems von Gangopadhyay, Tryambak, Ramanan, Vikram, Akintayo, Adedotun, K Boor, Paige, Sarkar, Soumalya, Chakravarthy, Satyanarayanan R, Sarkar, Soumik

“… highlights•A novel semi-supervised deep learning architecture for early detection of combustion instability.•Capturing transition from a stable to an unstable …”
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Stable 3D Deep Convolutional Autoencoder Method for Ultrasonic Testing of Defects in Polymer Composites von Liu, Yi, Yu, Qing, Liu, Kaixin, Zhu, Ningtao, Yao, Yuan

“… In this study, a stable three-dimensional deep convolutional autoencoder (3D-DCA) was developed to identify defects in polymer composites …”
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