Results 11 to 20 of about 207,018 (117)
PENDUGAAN DATA HILANG DENGAN MENGGUNAKAN DATA AUGMENTATION
Media Statistika, 2011 Data augmentation is a method for estimating missing data. It is a special case of Gibbs sampling which has two important steps. The first step is imputation or I-step where the missing data is generated based on the conditional distributions for missing
Mesra Nova, Moch. Abdul Mukid
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Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks
Sensors, 2021 Deep learning (DL) algorithms have become an increasingly popular choice for image classification and segmentation tasks; however, their range of applications can be limited.
Justin Lo +3 more
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Unsupervised learning using topological data augmentation
Physical Review Research, 2020 Unsupervised machine learning is a cornerstone of artificial intelligence as it provides algorithms capable of learning tasks, such as classification of data, without explicit human assistance.
Oleksandr Balabanov, Mats Granath
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Current Directions in Biomedical Engineering, 2021 Data augmentation is a common method to make deep learning assessible on limited data sets. However, classical image augmentation methods result in highly unrealistic images on ultrasound data.
Wulff Daniel +3 more
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Augmentation leak-prevention scheme using an auxiliary classifier in GAN-based image generation
Journal of King Saud University: Computer and Information Sciences, 2023 Although a generative adversarial network (GAN) can generate realistic and distinct images, it requires numerous training data. Data augmentation is a popular method of incrementing data using various augmentation operations.
Jonghwa Shim +3 more
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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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Informatics in Medicine Unlocked, 2021 Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets, networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training ...
Saman Motamed +2 more
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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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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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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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