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Each iteration of the MCMC includes numerous updates on global parameters, performed by the master core, whereas compute cores perform several updates of a truncated stick-breaking representation of the Dirichlet process (see Lartillot et al., 2013, for details), and sample the data augmentations.
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Additionally, data augmentation was applied, where each training sample was augmented by two randomly shifted (≤15 pxl) and two randomly rotated (≤8°) versions in each epoch.
Counterexample-Guided Data Augmentation.
Data augmentation via Lévy processes.
We expand this labeled set via data augmentation.
After data augmentation our training set consisted of 6000 images saved in HDF5 data format.
Supplementary Figure 3 Training data augmentation through random smooth elastic deformation.
The data augmentation for training the neural network was done using Python.
The program for data augmentation is provided as a Python script.
Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy.
From EM to Data Augmentation: The Emergence of MCMC Bayesian Computation in the 1980s.
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