Results 21 to 30 of about 588,587 (281)
RSMDA: Random Slices Mixing Data Augmentation
Applied Sciences, 2023 Advanced data augmentation techniques have demonstrated great success in deep learning algorithms. Among these techniques, single-image-based data augmentation (SIBDA), in which a single image’s regions are randomly erased in different ways, has shown ...
Teerath Kumar +3 more
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Text Data Augmentation for Deep Learning
Journal of Big Data, 2021 Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development.
Connor Shorten +2 more
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Data augmentation and semi-supervised learning for deep neural networks-based text classifier [PDF]
, 2020 User feedback is essential for understanding user needs. In this paper, we use free-text obtained from a survey on sleep-related issues to build a deep neural networks-based text classifier.
Devlin Jacob +5 more
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Spatio-Temporal Data Augmentation for Visual Surveillance
IEEE Access, 2021 Visual surveillance aims to detect a foreground object using a continuous image acquired from a fixed camera. Recent deep learning methods based on supervised learning show superior performance compared to classical background subtraction algorithms ...
Jae-Yeul Kim, Jong-Eun Ha
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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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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
doaj +1 more source
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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Data augmentation for galaxy density map reconstruction [PDF]
, 2011 The matter density is an important knowledge for today cosmology as many phenomena are linked to matter fluctuations. However, this density is not directly available, but estimated through lensing maps or galaxy surveys.
Dupé, François-Xavier +2 more
core +6 more sources
Data Augmentation for Low-Resource Neural Machine Translation [PDF]
, 2017 The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora. For low-resource language pairs this is not the case, resulting in poor translation quality. Inspired by work in computer vision, we
Bisazza, Arianna +2 more
core +2 more sources