Search Results - Residual learning conventional denoising autoencoder

Deep Residual Autoencoders for Expectation Maximization-Inspired Dictionary Learning by Tolooshams, Bahareh, Dey, Sourav, Ba, Demba

“… Specifically, we leverage the interpretation of the alternating-minimization algorithm for dictionary learning as an approximate expectation-maximization algorithm to develop autoencoders that enable…”
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Deep Convolutional Denoising Autoencoders with Network Structure Optimization for the High-Fidelity Attenuation of Random GPR Noise by Feng, Deshan, Wang, Xiangyu, Wang, Xun, Ding, Siyuan, Zhang, Hua

“…). In this paper, a novel network structure for convolutional denoising autoencoders (CDAEs) was proposed to effectively resolve various problems in the noise attenuation…”
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Background Prior-Based Salient Object Detection via Deep Reconstruction Residual by Junwei Han, Dingwen Zhang, Xintao Hu, Lei Guo, Jinchang Ren, Feng Wu

“… Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand-designed features and their distinctiveness…”
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Heart rate estimation for U-Net and LSTM models combining multiple attention mechanisms by Li, Ahui, Cai, Jun

“…•Innovative DRL-Unet Framework:○Combines Denoising Autoencoders, U-Net, LSTM, and Multi-Head Attention for heart rate estimation in complex noise environments…”
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Near-surface defect detection in ultrasonic testing using domain-knowledge-informed self-supervised learning by Jeon, Minsu, Choi, Minseok, Choi, Wonjae, Ha, Jong Moon, Oh, Hyunseok

“…•Self-supervised learning method for near-surface defect detection in ultrasonic testing…”
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Deep Residual U-Net Autoencoder with Weighted Overlapping Reconstruction for EMG Signal Denoising by Mehmood, Atif, Wiora, Jozef

“… This paper introduces an advanced deep learning framework for EMG denoising, centred on a U-Net-inspired convolutional autoencoder with integrated residual blocks and skip connections…”
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Cosaliency Detection Based on Intrasaliency Prior Transfer and Deep Intersaliency Mining by Zhang, Dingwen, Han, Junwei, Han, Jungong, Shao, Ling

“… For the intrasaliency prior transfer, we build a stacked denoising autoencoder (SDAE) to learn the saliency prior knowledge from auxiliary annotated data sets and then transfer…”
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Investigation of the effectiveness of a classification method based on improved DAE feature extraction for hepatitis C prediction by Zhang, Lin, Wang, Jixin, Chang, Rui, Wang, Weigang

“…) and applies it to hepatitis C disease detection. Conventional denoising autoencoder introduces random noise at the input layer of the encoder…”
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Deep learning-based motion artifact removal in functional near-infrared spectroscopy by Gao, Yuanyuan, Chao, Hanqing, Cavuoto, Lora, Yan, Pingkun, Kruger, Uwe, Norfleet, Jack E., Makled, Basiel A., Schwaitzberg, Steven, De, Suvranu, Intes, Xavier

“… To the best of our knowledge, this is the first investigation to report on the use of a denoising autoencoder (DAE…”
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Augmenting deviation of faults from the normal using fault assistant Gaussian mixture prior variational autoencoder by Lee, Yi Shan, Chen, Junghui

“…•Abnormal data are used to augment the deviation of the fault from the normal.•Non-negative information sharing and transferring improve model…”
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Deep Residual Autoencoders for Expectation Maximization-inspired Dictionary Learning by Tolooshams, Bahareh, Dey, Sourav, Demba Ba

“… Specifically, we leverage the interpretation of the alternating-minimization algorithm for dictionary learning as an approximate Expectation-Maximization algorithm to develop autoencoders that enable…”
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An End-to-End Self-Supervised Contrast Learning Network Embedded With Prior Knowledge for Laser Doppler Signals Denoising by Li, Anqi, Bao, Yu'an, Zhang, Faye, Peng, Chang, Liu, Xiaolin, Qiu, Shizhen, Liu, Fuzheng, Jiang, Mingshun

“… Conventional laser Doppler vibrometer (LDV) signal denoising methods encounter several challenges, including intricate parameter selection, limited real-time performance, incomplete noise suppression, and inadequate generalization capability…”
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Multi-Branch Network for Color Image Denoising Using Dilated Convolution and Attention Mechanisms by Duong, Minh-Thien, Nguyen Thi, Bao-Tran, Lee, Seongsoo, Hong, Min-Cheol

“… First, the proposed network is designed based on a conventional autoencoder to learn multi-level contextual features from input images…”
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Denoising method for Φ-OTDR systems based on deep non-negative matrix factorization and non-local means filtering by Zhang, Shihe, Cheng, Yafeng, Ming, Changpeng, Wang, Chenxu, Wang, Hanyong, Qian, Lei, Dong, Lei, Luo, Ming, Liu, Wu, Li, Hanbing, Huang, Tianye, Li, Xiang

“… seriously impact the detection and localization accuracy of weak signals. To address these issues, this study proposes a novel denoising method that combines Deep Autoencoder-like Nonnegative Matrix Factorization (DANMF…”
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Attenuation of marine seismic interference noise employing a customized U‐Net by Sun, Jing, Slang, Sigmund, Elboth, Thomas, Larsen Greiner, Thomas, McDonald, Steven, Gelius, Leiv‐J.

“… In the case of conventional images, autoencoders are frequently employed for denoising purposes…”
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심층 학습을 이용한 물리탐사 자료 잡음 제거 기술 소개 by Desy Caesary, 조아현, Ahyun Cho, 유희은, Huieun Yu, 정인석, Inseok Joung, 송서영, Seo Young Song, 조성오, Sung Oh Cho, 김빛나래, Bitnarae Kim, 남명진, Myung Jin Nam

“… Conventional denoising methods such as wavelet transform and filtering techniques, are based on subjective human investigation, which is computationally inefficient and time-consuming…”
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SPARSE-OTFS-Net: A Sparse Robust OTFS Signal Detection Algorithm for 6G Ubiquitous Coverage by Ling, Yunzhi, Xu, Jun

“… frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion…”
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Deep architecture neural network-based real-time image processing for image-guided radiotherapy by Mori, Shinichiro

“… Two types of residual network were designed: a convolutional autoencoder (rCAE) and a convolutional neural network (rCNN…”
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A comprehensive survey for Hyperspectral Image Classification: The evolution from conventional to transformers and Mamba models by Ahmad, Muhammad, Distefano, Salvatore, Khan, Adil Mehmood, Mazzara, Manuel, Li, Chenyu, Li, Hao, Aryal, Jagannath, Ding, Yao, Vivone, Gemine, Hong, Danfeng

“… While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven…”
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Attenuation of marine seismic interference noise employing a customized U-Net by Sun, Jing, Slang, Sigmund, Elboth, Thomas, Thomas Larsen Greiner, McDonald, Steven, Leiv-J Gelius

“… In case of conventional images, autoencoders are frequently employed for denoising purposes…”
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