Search Results - 3D conventional autoencoder*

A 3D lung lesion variational autoencoder by Li, Yiheng, Sadée, Christoph Y., Carrillo-Perez, Francisco, Selby, Heather M., Thieme, Alexander H., Gevaert, Olivier

“…In this study, we develop a 3D beta variational autoencoder (beta-VAE) to advance lung cancer imaging analysis, countering the constraints of conventional radiomics methods…”
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3D variational autoencoder for fingerprinting microstructure volume elements by White, Michael D., Atkinson, Michael D., Plowman, Adam J., Shanthraj, Pratheek

“… In this work, we present a 3D variational autoencoder (VAE) for encoding microstructure volume elements (VEs…”
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Synthetic Time-Series Data Generation for Smart Grids Using 3D Autoencoder GAN by Zhang, Guihai, Sikdar, Biplab

“…). Beyond the conventional GAN structure, the incorporation of both the Autoencoder and 3D-convolution processes enables a more comprehensive extraction of patterns…”
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A convolutional autoencoder model with weighted multi-scale attention modules for 3D skeleton-based action recognition by Khezerlou, F., Baradarani, A., Balafar, M.A.

“…The 3D skeleton sequences of action can be recognized based on series of meaningful movements including changes in the direction and geometry features of the body pose…”
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Deep Learning Representation using Autoencoder for 3D Shape Retrieval by Zhu, Zhuotun, Wang, Xinggang, Bai, Song, Yao, Cong, Bai, Xiang

“… To address these problems, we project 3D shapes into 2D space and use autoencoder for feature learning on the 2D images…”
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A combination method of stacked autoencoder and 3D deep residual network for hyperspectral image classification by Zhao, Jinling, Hu, Lei, Dong, Yingying, Huang, Linsheng, Weng, Shizhuang, Zhang, Dongyan

“…In comparison with conventional machine learning algorithms, deep learning can effectively express the deep features of remote sensing images…”
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Deep learning-based automated 3D inspection of helical gears using voxelized CAD models and 3D convolutional autoencoders by 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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Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data by Huang, Yi, Ren, Yihui, Yoo, Shinjae, Huang, Jin

“… The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE…”
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Unsupervised Machine Learning Method for the Phase Behavior of the Constant Magnetization Ising Model in Two and Three Dimensions by Jang, Inhyuk, Yethiraj, Arun

“… In this work we focus on the constant magnetization Ising model in two (2D) and three (3D) dimensions. While there have been many studies using machine learning…”
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3D-CNN and Autoencoder-Based Gas Detection in Hyperspectral Images by Ozdemir, Okan Bilge, Koz, Alper

“…) and autoencoder-based network, which is specially…”
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Convolutional autoencoder frameworks for projection multi-photon 3D printing by Jamil, Ishat Raihan, Johnson, Jason E., Xu, Xianfan

“…Projection multi-photon 3D printing is an emerging technique for fabricating micro-nano structures at exceptionally high speeds…”
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Contrastive Semantic-Aware Masked Autoencoder for Point Cloud Self-Supervised Learning by He, Yuan, Hu, Guyue, Yu, Shan

“…Masked Autoencoder (MAE) has shown remarkable potential in self-supervised representation learning for 3D point clouds…”
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The Use of 3D Convolutional Autoencoder in Fault and Fracture Network Characterization by 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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PonderV2: Improved 3D Representation With a Universal Pre-Training Paradigm by Zhu, Haoyi, Yang, Honghui, Wu, Xiaoyang, Huang, Di, Zhang, Sha, He, Xianglong, Zhao, Hengshuang, Shen, Chunhua, Qiao, Yu, He, Tong, Ouyang, Wanli

“…In contrast to numerous NLP and 2D vision foundational models, training a 3D foundational model poses considerably greater challenges…”
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Alzheimer's disease diagnostics by adaptation of 3D convolutional network by Hosseini-Asl, Ehsan, Keynton, Robert, El-Baz, Ayman

“… The 3D-CNN is built upon a 3D convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans…”
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Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network by Hosseini-Asl, Ehsan, Ghazal, Mohammed, Mahmoud, Ali, Aslantas, Ali, Shalaby, Ahmed M, Casanova, Manual F, Barnes, Gregory N, Gimel'farb, Georgy, Keynton, Robert, El-Baz, Ayman

“… The 3D-CNN is built upon a convolutional autoencoder, which is pre-trained to capture anatomical shape variations in structural brain MRI scans for source domain…”
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Enhancing representational learning for cloud robotic vision through explainable fuzzy convolutional autoencoder framework by Udendhran, R., Yamini, G., Badrinath, N., Jegathesh Amalraj, J., Suresh, A.

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3DPointCaps++: Learning 3D Representations with Capsule Networks by Zhao, Yongheng, Fang, Guangchi, Guo, Yulan, Guibas, Leonidas, Tombari, Federico, Birdal, Tolga

“… Unlike conventional 3D generative models, our algorithm aims for building a structured latent space where certain factors of shape variations, such as object parts, can be disentangled into independent sub-spaces…”
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3d Autoencoders For Feature Extraction In X-Ray Tomography by Tekawade, Aniket, Liu, Zhengchun, Kenesei, Peter, Bicer, Tekin, Carlo, Francesco De, Kettimuthu, Rajkumar, Foster, Ian

“…, porosity, particle size, and crack width) during continuous data acquisition. Segmentation of 2D or 3D images followed by quantitative measurement is the conventional…”
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Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders by Sajid, Muhammad, Razzaq Malik, Kaleem, Ur Rehman, Ateeq, Safdar Malik, Tauqeer, Alajmi, Masoud, Haider Khan, Ali, Haider, Amir, Hussen, Seada

“…Although the Transformer architecture has established itself as the industry standard for jobs involving natural language processing, it still has few uses in…”
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