Výsledky vyhledávání - "self-supervised autoencoder"

Self-supervised autoencoder network for robust heart rate extraction from noisy photoplethysmogram: Applying blind source separation to biosignal analysis Autor Webster, Matthew B., Lee, Dongheon, Lee, Joonnyong

“…Biosignals can be viewed as mixtures measuring particular physiological events, and blind source separation (BSS) aims to extract underlying source signals…”
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Auto-Encoders Derivatives on Different Occluded Face Images: Comprehensive Review and New Results Autor Masoudi, Azin, Ahmadi, Majid

“…This paper presents a novel approach for improving occluded face recognition performance using a family of autoencoders (AE) architectures. The proposed…”
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Self-Supervised Autoencoders for Visual Anomaly Detection Autor Bauer, Alexander, Nakajima, Shinichi, Müller, Klaus-Robert

“…We focus on detecting anomalies in images where the data distribution is supported by a lower-dimensional embedded manifold. Approaches based on autoencoders…”
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Self-supervised autoencoders for clustering and classification Autor Nousi, Paraskevi, Tefas, Anastasios

“…Clustering techniques aim at finding meaningful groups of data samples which exhibit similarity with regards to a set of characteristics, typically measured in…”
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Optimizing Self-Supervised Autoencoders for IoT Sensor Data Using Memory-Augmented Adaptive Simulated Annealing Autor Abdulrazzaq, Mohammed Majid, Ramaha, Nehad T. A., Ali Hameed, Alaa

“…The emergence of the Internet of Things (IoT) has given rise to unprecedented volumes of unlabeled sensor data due to its constantly evolving nature. This…”
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An Optimized Non-profiled Deep Learning-Based Power Analysis with Self-supervised Autoencoders Autor Kong, Fancong, Wang, Xiaohua, Xue, Chengbo, Pu, Kangran, Gao, Wei

“… challenges in practical application. In this paper, a novel non-profiled differential deep learning analysis architecture that incorporates a self-supervised autoencoder is proposed…”
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Neural models for detection and classification of brain states and transitions Autor Marin-Llobet, Arnau, Manasanch, Arnau, Dalla Porta, Leonardo, Torao-Angosto, Melody, Sanchez-Vives, Maria V.

“…) and a self-supervised autoencoder-based multimodal clustering algorithm. This approach distinguishes brain states such as slow oscillations, microarousals, and wakefulness with high confidence…”
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Extracting interpretable features for time series analysis: A Bag-of-Functions approach Autor Klopries, Hendrik, Schwung, Andreas

“…In this work we consider the problem of analyzing and predicting time series data using a Bag-of-Functions approach by a self supervised autoencoder…”
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A-SFS: Semi-supervised feature selection based on multi-task self-supervision Autor Qiu, Zhifeng, Zeng, Wanxin, Liao, Dahua, Gui, Ning

“…). Firstly, a multi-task self-supervised autoencoder is designed to uncover the hidden structural among features with the support…”
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Semi-supervised networks integrated with autoencoder and pseudo-labels propagation for structural condition assessment Autor Kong, Qingzhao, Xiong, Qingsong, Xiong, Haibei, He, Chang, Yuan, Cheng

“…•A novel semi-supervised network is proposed for structural condition assessment integrated with deep autoencoder and pseudo-label propagation.•An innovative…”
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Contrastive Attention-Based Network for Self-Supervised Point Cloud Completion Autor Kumari, Seema, Kumar, Preyum, Mandal, Srimanta, Raman, Shanmuganathan

“… In this paper, we propose an attention-driven, self-supervised autoencoder network that completes 3D point clouds from a single partial observation…”
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Graph Representation Learning Beyond Node and Homophily Autor Li, You, Lin, Bei, Luo, Binli, Gui, Ning

“…Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding. However, existing…”
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Two-Stream Architecture with Contrastive and Self-Supervised Attention Feature Fusion for Error-related Potentials Classification Autor Garrote, Luis, Perdiz, Joao, Yasemin, Mine, Pires, Gabriel, Nunes, Urbano J.

“… Its first stage is a self-supervised autoencoder architecture with a multi-head attention layer providing relevant latent features…”
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Urban Flood Mapping With Bitemporal Multispectral Imagery Via a Self-Supervised Learning Framework Autor Peng, Bo, Huang, Qunying, Vongkusolkit, Jamp, Gao, Song, Wright, Daniel B., Fang, Zheng N., Qiang, Yi

“…Near realtime flood mapping in densely populated urban areas is critical for emergency response. The strong heterogeneity of urban areas poses a big challenge…”
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Learning-Aided Evolutionary Algorithm for Solving Energy-Minimized Deadline-Constrained Task Scheduling Problem in Human-Cyber-Physical Systems Autor Cao, ZhengCai, Zhou, HaoRan, Lin, ChengRan

“…This work addresses an energy-minimized deadline-constrained task scheduling problem in human-cyber-physical systems. It consists of three subproblems:…”
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A Practical Non-Profiled Deep-Learning-Based Power Analysis with Hybrid-Supervised Neural Networks Autor Kong, Fancong, Wang, Xiaohua, Pu, Kangran, Zhang, Jingqi, Dang, Hua

“… The architecture incorporates a self-supervised autoencoder to enhance the features of power traces before they are utilized as training data for the supervised…”
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1126 Rational CAR T cell design via attention-based multiple instance learning of infusion product scRNA-seq data Autor Tsui, Kristin CY, Rodrigues, Kameron B, Zhan Xianghao, Chen, Yiyun, Mo, Kelvin C, Mackall, Crystal L, Miklos, David B, Gevaert Olivier, Good Zinaida

“…BackgroundChimeric antigen receptor (CAR) T therapies have transformed the treatment options for patients with hematological malignancies, yet more than 50% of…”
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Chapter 10 - Representation learning and retrieval Autor Tzelepi, Maria, Nousi, Paraskevi, Passalis, Nikolaos, Tefas, Anastasios

“…Recent advances in Deep Learning (DL), which inherently incorporate representation learning, have altered the landscape of representation learning tasks, such…”
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Optical and Acoustic Fusion in Borehole Imaging Logging Autor Li, Jiahang, Liang, Long, Gong, Yukun, He, Aoting, Zhu, Qi, Gao, Xuyang

“… The self-supervised autoencoder with convolutional neural networks (CNN) and transformer modules is established for feature extraction and reconstruction, where the fusion strategy is implemented in the independent stage…”
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End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation Autor Wang, Tianying, Puang, En Yen, Lee, Marcus, Jing, Wei, Wu, Yan

“… The proposed method learns keypoints from camera images as the state representation, through a self-supervised autoencoder architecture…”
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