Výsledky vyhľadávania - "autoencoder"

A comprehensive survey on design and application of autoencoder in deep learning Autor Li, Pengzhi, Pei, Yan, Li, Jianqiang

“…Autoencoder is an unsupervised learning model, which can automatically learn data features from a large number of samples and can act as a dimensionality…”
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Dynamical System Autoencoders Autor He, Shiquan, Paffenroth, Randy, Cava, Olivia, Dunham, Cate

“…Autoencoders represent a significant category of deep learning models and are widely utilized for dimensionality reduction. However, standard Autoencoders are…”
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Stacked Spatial-Temporal Autoencoder for Quality Prediction in Industrial Processes Autor Yan, Feng, Yang, Chunjie, Zhang, Xinmin

“…Nowadays, data-driven soft sensors have become a mainstream for the key performance indicators prediction, which guarantees the safety and stability of the…”
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Autoencoders and their applications in machine learning: a survey Autor Berahmand, Kamal, Daneshfar, Fatemeh, Salehi, Elaheh Sadat, Li, Yuefeng, Xu, Yue

“…Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction…”
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Transformer graph variational autoencoder for generative molecular design Autor Nguyen, Trieu, Karolak, Aleksandra

“…In the field of drug discovery, the generation of new molecules with desirable properties remains a critical challenge. Traditional methods often rely on…”
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druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico Autor Kadurin, Artur, Nikolenko, Sergey, Khrabrov, Kuzma, Aliper, Alex, Zhavoronkov, Alex

“…Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a…”
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Deep embedded clustering with distribution consistency preservation for attributed networks Autor Zheng, Yimei, Jia, Caiyan, Yu, Jian, Li, Xuanya

“…•A distribution consistency preserving deep embedded clustering model is proposed.•The model exploits GAE and AE to learn node representations and clusters…”
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Commonality Autoencoder: Learning Common Features for Change Detection From Heterogeneous Images Autor Wu, Yue, Li, Jiaheng, Yuan, Yongzhe, Qin, A. K., Miao, Qi-Guang, Gong, Mao-Guo

“…Change detection based on heterogeneous images, such as optical images and synthetic aperture radar images, is a challenging problem because of their huge…”
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DCTFormer: A Dual-Branch Transformer With Cloze Tests for Video Anomaly Detection Autor Chen, Pengzhan, Du, Shengdong, Zhao, Xiaole, Hu, Jie, Li, Jingjing, Li, Tianrui

“…Video anomaly detection is of critical importance in safety-critical scenarios. The key challenge is to effectively capture the spatio-temporal features of…”
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Image Compression Algorithm Based On Variational Autoencoder Autor Sun, Ying, Li, Lang, Ding, Yang, Bai, Jiabao, Xin, Xiangning

“…Variational Autoencoder (VAE), as a kind of deep hidden space generation model, has achieved great success in performance in recent years, especially in image…”
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DAEN: Deep Autoencoder Networks for Hyperspectral Unmixing Autor Su, Yuanchao, Li, Jun, Plaza, Antonio, Marinoni, Andrea, Gamba, Paolo, Chakravortty, Somdatta

“…Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral…”
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Hyperspectral Anomaly Detection with Guided Autoencoder Autor Xiang, Pei, Ali, Shahzad, Jung, Soon Ki, Zhou, Huixin

“…Recently, autoencoder-based hyperspectral anomaly detection methods have demonstrated excellent performance on hyperspectral images (HSIs). The autoencoder…”
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Unsupervised Unknown Radar Waveform Detection Autor Yoon, Jaehyeok, Nam, Haewoon

“…This paper proposes a high-accuracy method for detecting unknown radar waveforms using an unsupervised denoising network. The proposed method integrates a Deep…”
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Auto-Encoders Derivatives on Different Occluded Face Images: Comprehensive Review and New Results Autor Masoudi, Azin, Ahmadi, Majid

“…This paper presents a novel approach for improving occluded face recognition performance using a family of autoencoders (AE) architectures. The proposed…”
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Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis Autor Shi, Mingkuan, Ding, Chuancang, Wang, Rui, Song, Qiuyu, Shen, Changqing, Huang, Weiguo, Zhu, Zhongkui

“…Intelligent fault diagnosis based on deep learning (DL) has been widely used in various engineering practices. However, when confronting massive unlabeled…”
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Fast deep autoencoder for federated learning Autor Novoa-Paradela, David, Fontenla-Romero, Oscar, Guijarro-Berdiñas, Bertha

“…•A fast and privacy-preserving training algorithm for deep autoencoders is proposed.•The encoder block of the autoencoder is trained using Singular Value…”
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Autoencoder Artificial Neural Network Model for Air Pollution Index Prediction Autor Basir, Nor Irwin, Tan, Kathlyn Kaiyun, Djarum, Danny Hartanto, Ahmad, Zainal, Vo, Dai-Viet N., Jie, Zhang

“…Air pollution, a significant global challenge driven by industrialization, urbanization, and population growth, is caused by the emission of harmful gases,…”
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Adversarial Autoencoder Network for Hyperspectral Unmixing Autor Jin, Qiwen, Ma, Yong, Fan, Fan, Huang, Jun, Mei, Xiaoguang, Ma, Jiayi

“…Spectral unmixing (SU), which refers to extracting basic features (i.e., endmembers) at the subpixel level and calculating the corresponding proportion (i.e.,…”
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Dual Branch Learning with Prior Information for Surface Anomaly Detection Autor Wang, Shuyuan, Lv, Chengkan, Zhang, Zhengtao, Wei, Xueyan

“…Visual surface anomaly detection focuses on the classification and location of regions that deviate from the normal appearance, and generally, only normal…”
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Crafting imperceptible on-manifold adversarial attacks for tabular data Autor He, Zhipeng, Stevens, Alexander, Ouyang, Chun, De Smedt, Johannes, Barros, Alistair, Moreira, Catarina

“…Adversarial attacks on tabular data present unique challenges due to the heterogeneous nature of mixed categorical and numerical features. Unlike images where…”
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