Search Results - "Traditional Autoencoder"

Image Compression Algorithm Based On Variational Autoencoder by 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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Improve the Traditional Autoencoder Student Classroom Behavior Recognition Algorithm by Zhang, Man, Wei, Yan

“…Most of the existing behavior recognition methods are aimed at the dynamic behavior in action. When applied to the static behavior recognition environment in…”
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Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning by Senol, Nurettin Selcuk, Rasheed, Amar, Baza, Mohamed, Alsabaan, Maazen

“…—Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure…”
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MAMA Net: Multi-Scale Attention Memory Autoencoder Network for Anomaly Detection by Chen, Yurong, Zhang, Hui, Wang, Yaonan, Yang, Yimin, Zhou, Xianen, Wu, Q. M. Jonathan

“…Anomaly detection refers to the identification of cases that do not conform to the expected pattern, which takes a key role in diverse research areas and…”
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DeepFall: Non-Invasive Fall Detection with Deep Spatio-Temporal Convolutional Autoencoders by Nogas, Jacob, Khan, Shehroz S., Mihailidis, Alex

“…Human falls rarely occur; however, detecting falls is very important from the health and safety perspective. Due to the rarity of falls, it is difficult to…”
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A novel multichannel sparse convolutional autoencoder for electrocardiogram signal compression by Bekiryazıcı, Tahir, Damkacı, Mehmet, Aydemir, Gürkan, Gürkan, Hakan

“… Unlike traditional autoencoder-based methods, the first channel in the model remains unconstrained, while increasing levels of sparsity constraints are imposed on the remaining channels…”
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Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings by Zhu, Haiping, Cheng, Jiaxin, Zhang, Cong, Wu, Jun, Shao, Xinyu

“… Different from the traditional autoencoder, the proposed sPSDAE model, including a fully connected autoencoder network, uses the superior features extracted in all the previous layers to participate…”
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ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets by Shen, Yafei, Zhang, Tao, Liu, Zhiwei, Kostelidou, Kalliopi, Xu, Ying, Yang, Ling

“…), which disrupt latent correlations between dimensions, and high leverage points (HLP), which deviate from the norm but go undetected by traditional autoencoder-based methods…”
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Unsupervised anomaly detection for pome fruit quality inspection using X-ray radiography by Tempelaere, Astrid, He, Jiaqi, Van Doorselaer, Leen, Verboven, Pieter, Nicolai, Bart, Valerio Giuffrida, Mario

“…•Our model outperforms the traditional autoencoder architecture. A novel fully convolutional autoencoder (convAE…”
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Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders by Khan, Shehroz S., Taati, Babak

“…•Investigated the use of Autoencoders to learn generic features from wearable devices.•Proposed channel-wise ensemble approaches for Autoencoders to identify…”
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Enhancing yield prediction from plot-level satellite imagery through genotype and environment feature disentanglement by Powadi, Anirudha A., Jubery, Talukder Z., Tross, Michael, Shrestha, Nikee, Coffey, Lisa, Schnable, James C., Schnable, Patrick S., Ganapathysubramanian, Baskar

“…Accurately predicting yield during the growing season enables improved crop management and better resource allocation for both breeders and growers. Existing…”
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Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction by Zhang, Richard, Isola, Phillip, Efros, Alexei A.

“…We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning…”
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Representation learning via a semi-supervised stacked distance autoencoder for image classification by Hou, Liang, Luo, Xiao-yi, Wang, Zi-yang, Liang, Jun

“… The proposed method is based on the traditional autoencoder, incorporating the “distance” information between samples from different categories…”
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Combining KNN with AutoEncoder for Outlier Detection by Liu, Shu-Zheng, Ma, Shuai, Chen, Han-Qing, Cui, Li-Zhen, Ding, Jie

“…K -nearest neighbor ( K NN) is one of the most fundamental methods for unsupervised outlier detection because of its various advantages, e.g., ease of use and…”
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Unsupervised anomaly detection in hourly water demand data using an asymmetric encoder–decoder model by Yan, Jieru, Tao, Tao

“… Different from the symmetric structure of a traditional autoencoder where a signal is reproduced…”
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A deep stacked random vector functional link network autoencoder for diagnosis of brain abnormalities and breast cancer by Nayak, Deepak Ranjan, Dash, Ratnakar, Majhi, Banshidhar, Pachori, Ram Bilas, Zhang, Yudong

“…•A deep stacked RVFL autoencoder is proposed for diagnosis of brain abnormalities.•The proposed SRVFL-AE network is free from additional fine-tuning step.•It…”
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Anomaly detection in images with shared autoencoders by Jia, Haoyang, Liu, Wenfen

“… The first stage is roughly the same as the traditional autoencoder (AE) training, using the reconstruction loss of images and latent vectors for training…”
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CTNet: an efficient coupled transformer network for robust hyperspectral unmixing by Meng, Fanlei, Sun, Haixin, Li, Jie, Xu, Tingfa

“…) tasks, addressing key limitations of traditional autoencoder (AE) frameworks. Traditional AEs, consisting of an encoder and a decoder, effectively learn…”
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Multi-label learning with kernel extreme learning machine autoencoder by Cheng, Yusheng, Zhao, Dawei, Wang, Yibin, Pei, Gensheng

“…In multi-label learning, in order to improve the accuracy of classification, many scholars have considered the relationship between features and features,…”
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Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network by Li, Nanjun, Chang, Faliang, Liu, Chunsheng

“…Automatic detection of human-related anomalous events in surveillance videos is challenging, owing to unclear definition of anomalies and insufficiency of…”
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