Results 21 to 30 of about 24,031,398 (300)
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 for Electrocardiograms
CoRR, 2022 Neural network models have demonstrated impressive performance in predicting pathologies and outcomes from the 12-lead electrocardiogram (ECG). However, these models often need to be trained with large, labelled datasets, which are not available for many predictive tasks of interest.
Aniruddh Raghu +4 more
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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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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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An Empirical Survey of Data Augmentation for Limited Data Learning in NLP [PDF]
, 2023 NLP has achieved great progress in the past decade through the use of neural models and large labeled datasets. The dependence on abundant data prevents NLP models from being applied to low-resource settings or novel tasks where significant time, money ...
Derek Tam +4 more
core +1 more source
A Data Augmentation Algorithm for Trajectory Data
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Methods for Enriched Mobility Data: Emerging issues and Ethical perspectives 2023, 2023 The growing prevalence of location-based devices has resulted in a signi!cant abundance of location data from various tracking vendors. Nevertheless, a noticeable de!cit exists regarding readily accessible, extensive, and publicly available datasets for research purposes, primarily due to privacy concerns and ownership constraints.
J. Haranwala, Yaksh +3 more
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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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Virtual data augmentation method for reaction prediction in small dataset scenario [PDF]
, 2022 To improve the performance of data-driven reaction prediction models, a new data augmentation method for augmenting data volumes is presented that aims to add fake data in training dataset.
Yejian, Wu +8 more
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Augmentation of adaptation data [PDF]
Interspeech 2010, 2010 Linear regression based speaker adaptation approaches can improve Automatic Speech Recognition (ASR) accuracy significantly for a target speaker. However, when the available adaptation data is limited to a few seconds, the accuracy of the speaker adapted models is often worse compared with speaker independent models.
Vipperla, Ravi Chander +2 more
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Data Augmentation for Speech Separation
SSRN Electronic Journal, 2022 Deep learning models have advanced the state of the art of monaural speech separation. However, the performance of a separation model considerably decreases when tested on unseen speakers and noisy conditions. Separation models trained with data augmentation generalize better to unseen conditions.
Alex A. +3 more
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