Results 11 to 20 of about 599,022 (331)
Counterexample-Guided Data Augmentation [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 We present a novel framework for augmenting data sets for machine learning based on counterexamples. Counterexamples are misclassified examples that have important properties for retraining and improving the model. Key components of our framework include
Dreossi, Tommaso +5 more
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Data Augmentation for Text Generation Without Any Augmented Data [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021 Accepted into the main conference of ACL ...
Bi, Wei, Li, Huayang, Huang, Jiacheng
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Unsupervised Data Augmentation with Naive Augmentation and without Unlabeled Data [PDF]
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 Unsupervised Data Augmentation (UDA) is a semi-supervised technique that applies a consistency loss to penalize differences between a model's predictions on (a) observed (unlabeled) examples; and (b) corresponding 'noised' examples produced via data augmentation.
Lowell, David +3 more
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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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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
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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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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