Results 31 to 40 of about 599,022 (331)
An adaptive fusion-based data augmentation method for abstract dialogue summarization [PDF]
PeerJ Computer ScienceThe dialogue summarization is necessary for information retrieval, and the training of abstract dialogue summarization models heavily rely on large amounts of labeled data.
Weihao Li +4 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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Empirical copula-based data augmentation for mixed-type datasets: a robust approach for synthetic data generation [PDF]
PeerJ Computer ScienceData augmentation is a critical technique for enhancing model performance in scenarios with limited, sparse, or imbalanced datasets. While existing methods often focus on homogeneous data types (e.g., continuous-only or categorical-only), real-world ...
Mohsen Ben Hassine, Lamine Mili
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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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FMRI Data Augmentation Via Synthesis [PDF]
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 2019 We present an empirical evaluation of fMRI data augmentation via synthesis. For synthesis we use generative mod-els trained on real neuroimaging data to produce novel task-dependent functional brain images. Analyzed generative mod-els include classic approaches such as the Gaussian mixture model (GMM), and modern implicit generative models such as the ...
Zhuang, Peiye +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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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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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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, 2020 We propose a novel data augmentation method `GridMask' in this paper. It utilizes information removal to achieve state-of-the-art results in a variety of computer vision tasks. We analyze the requirement of information dropping. Then we show limitation of existing information dropping algorithms and propose our structured method, which is simple and ...
Chen, Pengguang +4 more
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Implicit Semantic Data Augmentation for Hand Pose Estimation
IEEE Access, 2022 Data augmentation is a well-known technique used for improving the generalization performance of modern neural networks. After the success of several traditional random data augmentation for images (including flipping, translation, or rotation), a recent
Kyeongeun Seo +3 more
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