Search Results - transformer residual autoencoder

Aircraft dynamics modeling at high angle of attack incorporating residual transformer autoencoder and physical mechanisms by Ma, Jinyi, Zhu, Qianqian, Xue, Tao, Ai, Jianliang, Dong, Yiqun

“… Second, a Residual Transformer (ResTrans) autoencoder is designed to extract temporal and spatial features from flight motion history under high-AOA conditions…”
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A residual autoencoder-based transformer for fault detection of multivariate processes by Shang, Jilin, Yu, Jianbo

“… In this paper, a new transformer model, residual autoencoder-based transformer, is proposed for process fault detection…”
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DCTFormer: A Dual-Branch Transformer With Cloze Tests for Video Anomaly Detection by Chen, Pengzhan, Du, Shengdong, Zhao, Xiaole, Hu, Jie, Li, Jingjing, Li, Tianrui

“… Firstly, we design a novel module TRAECT (Transformer-based Residual Autoencoder with Cloze Tests…”
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Towards Predicting the Measurement Noise Covariance with a Transformer and Residual Denoising Autoencoder for GNSS/INS Tightly-Coupled Integrated Navigation by Xu, Hongfu, Luo, Haiyong, Wu, Zijian, Wu, Fan, Bao, Linfeng, Zhao, Fang

“… In this article, we propose an adaptive measurement noise estimation algorithm using a transformer and residual denoising autoencoder (RDAE…”
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Residual Vision Transformer and Adaptive Fusion Autoencoders for Monocular Depth Estimation by Yang, Wei-Jong, Wu, Chih-Chen, Yang, Jar-Ferr

“… In the encoder, we construct a multi-scale feature extractor by mixing residual configurations of vision transformers to enhance both local and global information…”
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Study on transformer fault diagnosisbased on improved deep residual shrinkage network and optimized residual variational autoencoder by Yao, Haiyan, Xu, Yuefei, Guo, Qiang, Chen, Shizhe, Lu, Bin, Huang, Yuanjun

“… with complex and evolving fault patterns. In this study, a new method for transformer fault diagnosis based on improved deep residual shrinkage network (DRSN…”
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Vision Transformer and Residual Network-Based Autoencoder for RGBD Data Processing in Robotic Grasping of Noodle-Like Objects by Koomklang, Nattapat, Gamolped, Prem, Hayashi, Eiji

“…In this innovative study, a Vision Transformer and Residual Network-based Autoencoder is employed for the efficient encoding of RGBD data, aimed at enhancing robotic precision in grasping noodle-like objects…”
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Multivariate time series anomaly detection via separation, decomposition, and dual transformer-based autoencoder by Fu, Shiyuan, Gao, Xin, Li, Baofeng, Zhai, Feng, Lu, Jiansheng, Xue, Bing, Yu, Jiahao, Xiao, Chun

“…Multivariate time series usually have entangled temporal patterns and various anomaly types. Meanwhile, they often contain both continuous and discrete…”
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Predicting the Weight of Grappling Noodle-like Objects using Vision Transformer and Autoencoder by Hayashi, Eiji, Gamolped, Prem, Mowshowitz, Abbe, Koomklang, Nattapat

“… The proposed approach combines vision transformer and autoencoder techniques with action data and RGB-D encoding to enhance the capabilities of robots in manipulating objects with varying weights…”
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DUdetector: A dual-granularity unsupervised model for network anomaly detection by Geng, Haijun, Ma, Qi, Chi, Haotian, Zhang, Zhi, Yang, Jing, Yin, Xia

“… and Conv1d&MaxPool1d AutoEncoder with residual connection (abbr., CM&RC-AE) to realize…”
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Estimating the health status of lithium-ion batteries using features extracted from relaxation voltage by Wang, Dongyang, Wang, Bo, Ruan, Dianbo, Hong, Xiaobo

“… relaxation voltage features, autoencoder-based feature enhancement, and a transformer-inspired dual-residual network…”
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Comparative Analysis of Current Deep Learning Networks for Breast Lesion Segmentation in Ultrasound Images by Ferreira, Margarida R., Torres, Helena R., Oliveira, Bruno, Gomes-Fonseca, Joao, Morais, Pedro, Novais, Paulo, Vilaca, Joao L.

“… The methods were evaluated on a multi-center BUS dataset composed of three public datasets. Specifically, the U-Net, Dynamic U-Net, Semantic Segmentation Deep Residual Network with Variational…”
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Effective image compression using transformer and residual network for balanced handling of high and low-frequency information by Hu, Jianhua, Luo, Guixiang, Feng, Xiangfei, Yuan, Zhanjiang, Yang, Jiahui, Nie, Wei

“… To address this issue, the paper introduces a novel end-to-end autoencoder architecture for image compression based on the transformer and residual network…”
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TMAR: 3-D Transformer Network via Masked Autoencoder Regularization for Hyperspectral Sharpening by Dehghan, Zeinab, Yang, Jingxiang, Yazdi, Mehran, Khader, Abdolraheem, Xiao, Liang

“…) by fusing it with a high spatial resolution assistive image. Transformers have shown high efficiency in vision tasks due to their ability to learn global and long-range information…”
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T‐VAE: Transformer‐Based Variational AutoEncoder for Perceiving Anomalies in Multivariate Time Series Data by Li, Chang, Kiat, Yeo Chai, Jing, Jiwu, Long, Chun

“… challenges. In this paper, we propose a Transformer‐based Variational AutoEncoder (T‐VAE) for anomaly perception in multivariate…”
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SHAPE: A Simultaneous Header and Payload Encoding Model for Encrypted Traffic Classification by Dai, Jianbang, Xu, Xiaolong, Gao, Honghao, Wang, Xinheng, Xiao, Fu

“… To this end, we propose the SHAPE model (simultaneous header and payload encoding), which mainly consists of two autoencoders and a transformer layer, to improve model performance…”
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MaskCRT: Masked Conditional Residual Transformer for Learned Video Compression by Chen, Yi-Hsin, Xie, Hong-Sheng, Chen, Cheng-Wei, Gao, Zong-Lin, Benjak, Martin, Peng, Wen-Hsiao, Ostermann, Jorn

“… However, a recent study shows that it may perform worse than residual coding when the information bottleneck arises…”
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Cross-Modal Contrastive Masked AutoEncoder for Compressed Video Pre-Training by Li, Bing, Chen, Jiaxin, Li, Guohao, Zhang, Dongming, Bao, Xiuguo, Huang, Di

“…In this paper, we propose a novel Transformer based approach, namely Cross-modal Contrastive Masked AutoEncoder (C2MAE…”
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Anomaly Detection of Marine Diesel Engines: A Novel Approach using Transformer Neural Networks for Reconstruction and Residual Analysis by Qin Liang, Erik Vanem, Knut Erik Knutsen, Vilmar Æsøy, Houxiang Zhang

“…This paper proposes an unsupervised approach for anomaly detection in marine diesel engines using a transformer neural network based AutoEncoder (TAE…”
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Robust Authentication Analysis of Copyright Images through Deep Hashing Models with Self-supervision by Yang, Jaeyoung, Kim, Sooin, Lee, Sangwoo, Kim, Won-gyum, Kim, Donghoon, Hwang, Doosung

“…The increased usage of the internet and ICT has posed a significant challenge to protect copyrighted content due to advanced image forgery techniques that make…”
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