Suchergebnisse - denoising autoen-coder

An Effective Deep Learning Based Scheme for Network Intrusion Detection von Zhang, Hongpo, Wu, Chase Q., Gao, Shan, Wang, Zongmin, Xu, Yuxiao, Liu, Yongpeng

“… The proposed scheme employs a denoising autoencoder (DAE) with a weighted loss function for feature selection, which determines a limited number of important features for intrusion detection to reduce feature dimensionality …”
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Study on State of Health of Power Battery Prediction Using Denoising Autoencoder and Bidirectional Long Short-Term Memory Networks von Bao, Caijilahu, Wang, Xin, Ma, Zhiqiang, Liu, Guangchen, Gao, Jundong, Gao, Wenjun

“… Firstly, feature learning and extraction of the power battery data are performed using a denoising auto encoder, which is able to extract key information and capture variations over time from the data …”
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Recurrent Nerual Imaging: An Evolutionary Approach for Mixed Possion-Gaussian Image Denoising von Ranganath, Aditya, DeGuchy, Omar, Santiago, Fabian, Singhal, Mukesh, Marcia, Roummel

“… Numerical experiments demonstrate that our proposed RNN approach outperforms convolutional autoen-coder methods for denoising and upsampling low-resolution images …”
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Dynimp: Dynamic Imputation for Wearable Sensing Data through Sensory and Temporal Relatedness von Huo, Zepeng, Ji, Taowei, Liang, Yifei, Huang, Shuai, Wang, Zhangyang, Qian, Xiaoning, Mortazavi, Bobak

“… We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis and then feeding the data into a LSTM-based denoising autoen-coder …”
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A Black-Box Adversarial Attack via Deep Reinforcement Learning on the Feature Space von Li, Lyue, Rezapour, Amir, Tzeng, Wen-Guey

“… In this paper we propose a novel black-box adversarial attack by using the reinforcement learning to learn the characteristics of the target classifier C. Our …”
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Automatic Enhancement of Two-Dimensional Gel electrophoresis images using Denoising Autoencoder von Ahmed, Aya Saleh, El-Behaidy, Wessam H., Youssif, Aliaa A. A.

“… ) that strongly affect spot detection or pixel-based methods. Denoising autoen-coders (DAE) is a new approach in deep learning used in image denoising that has a challenging performance …”
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Fully convolutional denoising autoencoder for 3D scene reconstruction from a single depth image von Palla, Alessandro, Moloney, David, Fanucci, Luca

“… In this work, we propose a 3D scene reconstruction algorithm based on a fully convolutional 3D denoising autoencoder neural network …”
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A Comprehensive Exploration of Noise Robustness and Noise Compensation in ResNet and TDNN-based Speaker Recognition Systems von MohammadAmini, Mohammad, Matrouf, Driss, Bonatsre, Jean-Francois, Dowerah, Sandipana, Serizel, Romain, Jouvet, Denis

“… Our experimental results show that in all cases the ResNet system is more robust than TDNN. After that, a noise compensation task is done with denoising autoen-coder (DAE …”
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APT-LLM: Embedding-Based Anomaly Detection of Cyber Advanced Persistent Threats Using Large Language Models von Benabderrahmane, Sidahmed, Valtchev, Petko, Cheney, James, Rahwan, Talal

“… Advanced Persistent Threats (APTs) pose a ma-jor cybersecurity challenge due to their stealth and ability to mimic normal system behavior, making detection …”
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Deep Learning Hyperspectral Image Classification using Multiple Class-Based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations von Ball, John E., Wei, Pan

“… Herein, we present a system for hyperspectral image segmentation that utilizes multiple class-based denoising autoen-coders which are efficiently trained …”
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Orthogonal autoencoder regression for image classification von Yang, Zhangjing, Wu, Xinxin, Huang, Pu, Zhang, Fanlong, Wan, Minghua, Lai, Zhihui

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SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints von Li, Yahang, Wang, Zepeng, Lam, Fan

“… We present a novel method to enhance the SNR for multi-TE MR spectroscopic imaging (MRSI) data by integrating learned nonlinear low-dimensional model and …”
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Denoising Convolutional Variational Autoencoders-Based Feature Learning for Automatic Detection of Plant Diseases von Zilvan, Vicky, Ramdan, Ade, Suryawati, Endang, Kusumo, R. Budiarianto S., Krisnandi, Dikdik, Pardede, Hilman F.

“… Early detection is critical for maintaining quantity and quality of farming commodity. Currently, detection of plant diseases still requires human expertise …”
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Denoising Method for Wireless Communication Signals Based on Convolutional AutoEncoder von Min, Woonggyu, Kim, Jongseok, Jo, Ohyun

“… In this paper, we propose a method to effectively remove noise from signals by utilizing convolutional autoen-coder …”
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Deep-Learning-based Channel Estimation for Chaotic Wireless Communication von Yin, Hui-Ping, Zhao, Xiao-Hui, Yao, Jun-Ling, Ren, Hai-Peng

“… ) pre-trained by the stacked denoising autoencoder (SDAE) structure. The DNN learns the channel parameters by using the autocorrelation function (ACF …”
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Explainable Credit Scoring Through Generative Adversarial Networks von Han, Seongil

“… Credit scoring has been playing a vital role in mitigating financial risk that could affect the sustainability of financial institutions. An accurate and …”
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Quantum Error Correction with Quantum Autoencoders von Locher, David F., Cardarelli, Lorenzo, Müller, Markus

“… spatially correlated computational errors as well as qubit losses. We highlight that the denoising capabilities of quantum autoencoders are not limited to the protection of specific states but extend to the entire logical codespace …”
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Implementation of Ensemble Learning Methods in Autoencoder Architecture for Anomaly Detection in Natural Gas Pipeline Operational Data von Azzaka, Muhammad Rivaditya, Firman Ihsan, Aditya, Astuti, Widi

“… Research implements an ensemble of autoen-coders (EoAE) approach for detecting anomalies in natural gas pipeline operational data through integration of three complementary autoencoder architectures …”
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