Results 61 to 70 of about 9,759,453 (201)
Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification
Remote Sensing, 2021 For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance.
Wenning Wang, Xuebin Liu, Xuanqin Mou
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Fast Augmenting Paths by Random Sampling from Residual Graphs [PDF]
SIAM Journal on Computing, 2015 Summary: Consider an \(n\)-vertex, \(m\)-edge, undirected graph with integral capacities and max-flow value \(v\). We give a new \(\tilde{O}(m + nv)\)-time maximum flow algorithm. After assigning certain special sampling probabilities to edges in \(\tilde{O}(m)\) time, our algorithm is very simple: repeatedly find an augmenting path in a random sample ...
Karger, David R., Levine, Matthew S.
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Sampling rare event energy landscapes via birth-death augmented dynamics
Physical Review E, 2023 17 pages, 5 ...
Benjamin Pampel +3 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingSmall-sample learning improves the problem of limited labeled samples in hyperspectral image (HSI) classification to a greater extent, but still suffers from the severe problem of class imbalance, where minority classes are poorly learned and classified,
Ke Li +5 more
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, 2010 In recent years, a variety of extensions and refinements have been developed for data augmentation based model fitting routines. These developments aim to extend the application, improve the speed and/or simplify the implementation of data augmentation ...
Meng, Xiao-Li, van Dyk, David A.
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Side-Scan Sonar Image Augmentation Method Based on CC-WGAN
Applied SciencesThe utilization of deep learning algorithms for side-scan sonar target detection is impeded by the restricted quantity and representativeness of side-scan sonar (SSS) samples.
Junhui Zhu +4 more
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Simulating dysarthric speech for training data augmentation in clinical speech applications
, 2018 Training machine learning algorithms for speech applications requires large, labeled training data sets. This is problematic for clinical applications where obtaining such data is prohibitively expensive because of privacy concerns or lack of access.
Berisha, Visar +3 more
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TADA: phylogenetic augmentation of microbiome samples enhances phenotype classification [PDF]
Bioinformatics, 2019 AbstractMotivationLearning associations of traits with the microbial composition of a set of samples is a fundamental goal in microbiome studies. Recently, machine learning methods have been explored for this goal, with some promise. However, in comparison to other fields, microbiome data are high-dimensional and not abundant; leading to a high ...
Sayyari, Erfan +2 more
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Data Augmentation with Variational Autoencoders and Manifold Sampling [PDF]
, 2021 We propose a new efficient way to sample from a Variational Autoencoder in the challenging low sample size setting. This method reveals particularly well suited to perform data augmentation in such a low data regime and is validated across various standard and real-life data sets.
Chadebec, Clément +1 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 The classification of remote sensing images with high spatial resolution requires considerable training samples, but the process of sample making is slow and laborious.
Baikai Sui +3 more
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