Results 11 to 20 of about 588,587 (281)
Smart Augmentation Learning an Optimal Data Augmentation Strategy [PDF]
IEEE Access, 2017 A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks.
Joseph Lemley +2 more
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Distribution-preserving data augmentation [PDF]
PeerJ Computer Science, 2021 In the last decade, deep learning has been applied in a wide range of problems with tremendous success. This success mainly comes from large data availability, increased computational power, and theoretical improvements in the training phase.
Nurdan Ayse Saran +2 more
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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
openaire +2 more sources
Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation
Applied Sciences, 2021 The task of charge prediction is to predict the charge based on the fact description. Existing methods have a good effect on the prediction of high-frequency charges, but the prediction of low-frequency charges is still a challenge. Moreover, there exist
Peipeng Wang, Xiuguo Zhang, Zhiying Cao
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Survey on Sequence Data Augmentation
Jisuanji kexue yu tansuo, 2021 To pursue higher accuracy, the structure of deep learning model is getting more and more complex, with deeper and deeper network. The increase in the number of parameters means that more data are needed to train the model. However, manually labeling data
GE Yizhou, XU Xiang, YANG Suorong, ZHOU Qing, SHEN Furao
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Data Augmentation Method for AMR-to-Text Generation [PDF]
Jisuanji gongcheng, 2022 In the process of Abstract Meaning Representation(AMR)-to-text generation, the transformation from AMR graph to text is largely affected by the size of the corpus.A simple and effective dynamic data augmentation method is proposed to improve the ...
FU Yeqiang, LI Junhui
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Class-Adaptive Data Augmentation for Image Classification
IEEE Access, 2023 Data augmentation is a widely used regularization technique for improving the performance of convolutional neural networks (CNNs) in image classification tasks.
Jisu Yoo, Seokho Kang
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DEEPFAKE Image Synthesis for Data Augmentation
IEEE Access, 2022 Field of medical imaging is scarce in terms of a dataset that is reliable and extensive enough to train distinct supervised deep learning models. One way to tackle this problem is to use a Generative Adversarial Network to synthesize DEEPFAKE images to ...
Nawaf Waqas +4 more
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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
openaire +2 more sources