Results 41 to 50 of about 252,636 (309)
Federated Data Augmentation Algorithm for Non-independent and Identical Distributed Data [PDF]
Jisuanji kexue, 2022 In federated learning,the local data distribution of users changes with the location and preferences of users,the data under the non-independent and identical distributed(Non-IID) data may lack data of some label categories,which significantly affects ...
QU Xiang-mou, WU Ying-bo, JIANG Xiao-ling
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DIFFEOMORPHIC TRANSFORMS FOR DATA AUGMENTATION OF HIGHLY VARIABLE SHAPE AND TEXTURE OBJECTS [PDF]
, 2022 openIn questo documento viene analizzata una nuova tecnica utilizzata in ambito di Data Augmentation che pone le proprie basi sul morphing. L’addestramento di reti neurali convoluzionali (CNN) richiede una grossa mole di dati, i quali possono essere ...
LAZZARIN, FILIPPO
core
Brainwave Classification Using Covariance-Based Data Augmentation
IEEE Access, 2020 A brain-machine interface (BMI) is a technology that controls machines via brainwaves. In BMI, the performance of brainwave analysis is very important for achieving machine control that reflects the user's intention.
Wonseok Yang, Woochul Nam
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To Augment or Not to Augment? Data Augmentation in User Identification Based on Motion Sensors [PDF]
, 2020 Nowadays, commonly-used authentication systems for mobile device users, e.g. password checking, face recognition or fingerprint scanning, are susceptible to various kinds of attacks. In order to prevent some of the possible attacks, these explicit authentication systems can be enhanced by considering a two-factor authentication scheme, in which the ...
Cezara Benegui, Radu Tudor Ionescu
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Transfer Incremental Learning Using Data Augmentation
Applied Sciences, 2018 Deep learning-based methods have reached state of the art performances, relying on a large quantity of available data and computational power. Such methods still remain highly inappropriate when facing a major open machine learning problem, which ...
Ghouthi Boukli Hacene +4 more
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Data Augmentation For Label Enhancement
CoRR, 2023 Label distribution (LD) uses the description degree to describe instances, which provides more fine-grained supervision information when learning with label ambiguity. Nevertheless, LD is unavailable in many real-world applications. To obtain LD, label enhancement (LE) has emerged to recover LD from logical label.
Zhiqiang Kou +4 more
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Review of Image Data Augmentation in Computer Vision
Jisuanji kexue yu tansuo, 2021 Deep learning is a promising solution for computer vision at present. To solve the computer vision problem, it requires massive and high-quality image training datasets.
LIN Chengchuang, SHAN Chun, ZHAO Gansen, YANG Zhirong, PENG Jing, CHEN Shaojie, HUANG Runhua, LI Zhuangwei, YI Xusheng, DU Jiahua, LI Shuangyin, LUO Haoyu, FAN Xiaomao, CHEN Bingchuan
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Text Data Augmentation for the Korean Language
Applied Sciences, 2022 Data augmentation (DA) is a universal technique to reduce overfitting and improve the robustness of machine learning models by increasing the quantity and variety of the training dataset.
Dang Thanh Vu +3 more
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Image augmentation techniques for convolutional neural network [PDF]
, 2022 openUna delle più grandi sfide per le Reti Neurali Convoluzionali, soprattutto ora che vengono utilizzate ampiamente in svariati contesti, è la mancanza di training set adeguati per sessioni di training robuste e meno prone ad overfitting.
BRAVIN, RICCARDO
core
Dynamic causal model application on hierarchical human motor control estimation in visuomotor tasks
Frontiers in NeurologyIntroductionIn brain function research, each brain region has been investigated independently, and how different parts of the brain work together has been examined using the correlations among them.
Ningjia Yang +6 more
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