Results 21 to 30 of about 599,022 (331)
Data Augmentation for Low-Resource Neural Machine Translation [PDF]
, 2017 The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora. For low-resource language pairs this is not the case, resulting in poor translation quality. Inspired by work in computer vision, we
Bisazza, Arianna +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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Unsupervised learning using topological data augmentation
Physical Review Research, 2020 Unsupervised machine learning is a cornerstone of artificial intelligence as it provides algorithms capable of learning tasks, such as classification of data, without explicit human assistance.
Oleksandr Balabanov, Mats Granath
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Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks
Sensors, 2021 Deep learning (DL) algorithms have become an increasingly popular choice for image classification and segmentation tasks; however, their range of applications can be limited.
Justin Lo +3 more
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, 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 ...
Schneider, Nora +2 more
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ParticleAugment: Sampling-based data augmentation
Computer Vision and Image Understanding, 2023 8 ...
Tsaregorodtsev, Alexander +1 more
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Data augmentation for models based on rejection sampling [PDF]
, 2015 We present a data augmentation scheme to perform Markov chain Monte Carlo inference for models where data generation involves a rejection sampling algorithm.
Dunson, David, Lin, Lizhen, Rao, Vinayak
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Informatics in Medicine Unlocked, 2021 Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets, networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training ...
Saman Motamed +2 more
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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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Improving customer churn prediction by data augmentation using pictorial stimulus-choice data [PDF]
, 2012 The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus choice data and survey ...
Ballings, Michel +2 more
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