Results 11 to 20 of about 252,636 (309)
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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Data Augmentation for Plant Classification [PDF]
, 2017 Data augmentation plays a crucial role in increasing the number of training images, which often aids to improve classification performances of deep learning techniques for computer vision problems. In this paper, we employ the deep learning framework and determine the effects of several data-augmentation (DA) techniques for plant classification ...
Pornntiwa Pawara +3 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
doaj +1 more source
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
openaire +3 more sources
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
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
doaj +1 more source
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
openaire +2 more sources
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
doaj +1 more source
Text Data Augmentation for Deep Learning
Journal of Big Data, 2021 Natural Language Processing (NLP) is one of the most captivating applications of Deep Learning. In this survey, we consider how the Data Augmentation training strategy can aid in its development.
Connor Shorten +2 more
doaj +1 more source