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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A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective [PDF]
Neural Information Processing Systems, 2022 We propose the first unified theoretical analysis of mixed sample data augmentation (MSDA), such as Mixup and CutMix. Our theoretical results show that regardless of the choice of the mixing strategy, MSDA behaves as a pixel-level regularization of the ...
Chanwoo Park, Sangdoo Yun, Sanghyuk Chun
semanticscholar +1 more source
Remote Sensing Target Tracking in UAV Aerial Video Based on Saliency Enhanced MDnet
IEEE Access, 2019 Remote sensing target tracking in the aerial video from unmanned aerial vehicles (UAV) plays a key role in public security. As the UAV aerial video has rapid changes in scale and perspective, few pixels in the target region, and multiple similar ...
Fukun Bi +3 more
doaj +1 more source
Data augmentation for models based on rejection sampling [PDF]
Biometrika, 2016 6 figures.
Rao, Vinayak +2 more
openaire +3 more sources
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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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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Sample, Translate, Recombine: Leveraging Audio Alignments for Data Augmentation in End-to-end Speech Translation [PDF]
Annual Meeting of the Association for Computational Linguistics, 2022 End-to-end speech translation relies on data that pair source-language speech inputs with corresponding translations into a target language. Such data are notoriously scarce, making synthetic data augmentation by back-translation or knowledge ...
Tsz Kin Lam +2 more
semanticscholar +1 more source
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
openaire +2 more sources
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
doaj +1 more source