Results 1 to 10 of about 252,636 (309)
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
doaj +5 more sources
Smart Augmentation Learning an Optimal Data Augmentation Strategy
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
doaj +4 more sources
Advances in Neural Information Processing Systems 36, 2023 We propose a novel algorithm for data augmentation in nonlinear over-parametrized regression. Our data augmentation algorithm borrows from the literature on causality and extends the recently proposed Anchor regression (AR) method for data augmentation, which is in contrast to the current state-of-the-art domain-agnostic solutions that rely on the ...
Nora Schneider +2 more
openaire +4 more sources
Data Augmentation for Manipulation
Robotics: Science and Systems XVIII, 2022 The success of deep learning depends heavily on the availability of large datasets, but in robotic manipulation there are many learning problems for which such datasets do not exist. Collecting these datasets is time-consuming and expensive, and therefore learning from small datasets is an important open problem.
Peter Mitrano, Dmitry Berenson
openaire +3 more sources
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 ...
Wei Bi, Huayang Li, Jiacheng Huang 0005
openaire +2 more sources
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.
David Lowell +3 more
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source