Suchergebnisse - asymmetric denoising autoencoder

Multi-Type Missing Imputation of Time-Series Power Equipment Monitoring Data Based on Moving Average Filter–Asymmetric Denoising Autoencoder von Jiang, Ling, Gu, Juping, Zhang, Xinsong, Hua, Liang, Cai, Yueming

“… This leads to the poor quality and utilization difficulties of the collected data. To address this problem, this paper customizes methodology that combines an asymmetric denoising autoencoder (ADAE …”
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Two-stage noise aware training using asymmetric deep denoising autoencoder von Lee, Kang Hyun, Kang, Shin Jae, Kang, Woo Hyun, Kim, Nam Soo

“… Ever since the deep neural network (DNN)-based acoustic model appeared, the recognition performance of automatic speech recognition has been greatly improved …”
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A channel estimation method using denoising autoencoder for large-scale asymmetric backscatter systems von Jung, Chae Yoon, Kang, Jae-Mo, Kim, Dong In

“… In order to obtain channel data, we design denoising autoencoder which consists of encoder with Feedforward Neural Network (FNN …”
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Semi-Supervised Text Simplification with Back-Translation and Asymmetric Denoising Autoencoders von Zhao, Yanbin, Chen, Lu, Chen, Zhi, Yu, Kai

“… ), including denoising autoencoders for language modeling and automatic generation of parallel data by iterative back-translation …”
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Query-by-example surgical activity detection von Gao, Yixin, Vedula, S. Swaroop, Lee, Gyusung I., Lee, Mija R., Khudanpur, Sanjeev, Hager, Gregory D.

“… Our approach includes an unsupervised feature learning module using a stacked denoising autoencoder (SDAE …”
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Asymmetric Autoencoders: An NN alternative for resource-constrained devices in IoT networks von Gilbert, Mateus S., de Campos, Marcello L.R., Campista, Miguel Elias M.

“… Many solutions using Neural Networks (NNs) have emerged to address both issues, resorting to autoencoders to extract these redundancies to reduce data transmissions in IoT networks and to remove noise from data in general …”
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Deep convolutional autoencoder for the simultaneous removal of baseline noise and baseline drift in chromatograms von Kensert, Alexander, Collaerts, Gilles, Efthymiadis, Kyriakos, Van Broeck, Peter, Desmet, Gert, Cabooter, Deirdre

“… •Autoencoder (AEC) for the simultaneous removal of baseline noise and baseline drift …”
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Efficient Bearing Sensor Data Compression via an Asymmetrical Autoencoder with a Lifting Wavelet Transform Layer von Zhu, Xin, Cetin, Ahmet Enis

“… In this paper, a novel asymmetrical autoencoder with a lifting wavelet transform (LWT) layer is developed to compress bearing sensor data …”
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Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation von Grönroos, Stig-Arne, Virpioja, Sami, Kurimo, Mikko

“… We review these approaches in the context of an asymmetric-resource one-to-many translation task, in which the pair of target languages are related, with one being a very low-resource and the other …”
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DEMAE: Diffusion-Enhanced Masked Autoencoder for Hyperspectral Image Classification With Few Labeled Samples von Li, Ziyu, Xue, Zhaohui, Jia, Mingming, Nie, Xiangyu, Wu, Hao, Zhang, Mengxue, Su, Hongjun

“… Masked autoencoder (MAE), which is based on Transformer architecture, employs a "mask-reconstruction" strategy for training, allowing the model to be effective for downstream tasks …”
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DMAE-EEG: A Pretraining Framework for EEG Spatiotemporal Representation Learning von Zhang, Yifan, Yu, Yang, Li, Hao, Wu, Anqi, Chen, Xin, Liu, Jinfang, Zeng, Ling-Li, Hu, Dewen

“… To address these challenges, we develop a pretraining framework named DMAE-EEG, a denoising masked autoencoder for mining generalizable spatiotemporal representation from massive unlabeled EEG …”
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MAID: Model Attribution via Inverse Diffusion von Zhu, Luyu, Ye, Kai, Yao, Jiayu, Li, Chenxi, Zhao, Luwen, Cao, Yuxin, Wang, Derui, Hao, Jie

“… By employing the inverse diffusion process, we are able to utilize pre-trained Diffusion Models as Denoising Autoencoders, mapping images into a latent space and extracting …”
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Asymmetric Adaptation of Deep Features for Cross-Domain Classification in Remote Sensing Imagery von Ammour, Nassim, Bashmal, Laila, Bazi, Yakoub, Al Rahhal, M. M., Zuair, Mansour

“… Before the adaptation process, we feed the features obtained from a pretrained convolutional neural network to a denoising autoencoder (DAE …”
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Feature Learning for Multispectral Satellite Imagery Classification using Neural Architecture Search von Campbell, Roberto, Coltin, Brian, Furlong, P Michael, McMichael, Scott

“… Automated classification of remote sensing data is an integral tool for earth scientists, and deep learning has proven very successful at solving such …”
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Principal Component Wavelet Networks for Solving Linear Inverse Problems von Tiddeman, Bernard, Ghahremani, Morteza

“… —these are asymmetric problems, where the forward problem is easy to solve, but the inverse is difficult and often ill-posed …”
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Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation von Stig-Arne Grönroos, Virpioja, Sami, Kurimo, Mikko

“… We review these approaches in the context of an asymmetric-resource one-to-many translation task, in which the pair of target languages are related, with one being a very low-resource and the other …”
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Diffusion-Driven Domain Adaptation for Generating 3D Molecules von Hong, Haokai, Lin, Wanyu, Kay Chen Tan

“… As the domain shift is typically caused by the structure variations of molecules, e.g., scaffold variations, we leverage a designated equivariant masked autoencoder (MAE …”
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