Results 21 to 30 of about 322,902 (281)
, 2022 Data-Augmentation (DA) is known to improve performance across tasks and datasets. We propose a method to theoretically analyze the effect of DA and study questions such as: how many augmented samples are needed to correctly estimate the information encoded by that DA? How does the augmentation policy impact the final parameters of a model?
Balestriero, Randall +2 more
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Frontiers in Neuroscience, 2023 IntroductionDue to the lack of devices and the difficulty of gathering patients, the small sample size is one of the most challenging problems in functional brain network (FBN) analysis.
Qinghua Liu +3 more
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Processes, 2023 Despite substantial progress in the field of deep learning, overfitting persists as a critical challenge, and data augmentation has emerged as a particularly promising approach due to its capacity to enhance model generalization in various computer vision tasks.
Wen Liang, Youzhi Liang, Jianguo Jia
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A GAN-Based Augmentation Scheme for SAR Deceptive Jamming Templates with Shadows
Remote Sensing, 2023 To realize fast and effective synthetic aperture radar (SAR) deception jamming, a high-quality SAR deception jamming template library can be generated by performing sample augmentation on SAR deception jamming templates.
Shinan Lang +5 more
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GreedyCenters: Satellite imagery adaptive sampling method for artificial neural networks training [PDF]
E3S Web of Conferences, 2021 The one of many significant particularities of satellite imagery is large size of images within orders of magnitude exceeds capability of modern GPGPU to train neural networks on its full size.
Gvozdev Oleg
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Dynamic Data Augmentation Method for Hyperspectral Image Classification Based on Siamese Structure
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 At present, deep learning classification researches of hyperspectral usually focus on optimizing the classification model. In essence, most of them did not take special measures for the characteristics of the small sample and imbalanced category ...
Hongmin Gao +5 more
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Data augmentation for models based on rejection sampling [PDF]
Biometrika, 2016 6 figures.
Rao, Vinayak +2 more
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MetaAugment: Sample-Aware Data Augmentation Policy Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Automated data augmentation has shown superior performance in image recognition. Existing works search for dataset-level augmentation policies without considering individual sample variations, which are likely to be sub-optimal. On the other hand, learning different policies for different samples naively could greatly increase the computing cost.
Zhou, Fengwei +6 more
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Augmenting source code lines with sample variable values [PDF]
Proceedings of the 26th Conference on Program Comprehension, 2018 Source code is inherently abstract, which makes it difficult to understand. Activities such as debugging can reveal concrete runtime details, including the values of variables. However, they require that a developer explicitly requests these data for a specific execution moment.
Sulír, Matúš, Porubän, Jaroslav
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Variational Autoencoders for Data Augmentation in Clinical Studies
Applied Sciences, 2023 Sample size estimation is critical in clinical trials. A sample of adequate size can provide insights into a given population, but the collection of substantial amounts of data is costly and time-intensive.
Dimitris Papadopoulos +1 more
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