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By adding a supervised layer to a classical AE, and using the segmentation image of training samples as the supervised information, the model can learn the useful features to complete the semantic segmentation.
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Figure 5 Illustration of training images.
The paper ends with an exclusive story about a British connection in a plot to kill a Saudi king, and a striking image of train passengers being evacuated during an anti-terrorism operation in Brussels, on its final front page.
Unlike its counterparts in France and Germany, Renfe opted for a high-end service at a slightly higher price than comparable services in other countries, believing that the emphasis on service would appeal to Spanish travelers and help change the image of train travel.
Figure 1 Low-dimensional representations of training images obtained by PCA using the CMU PIE database.
Similar observations were illustrated in [9]. Figure 3 Low-dimensional representations of training images obtained by PCA and MPCA.
The other 70 images of training set are used as validation.
To avoid overfitting, we take a validation with a subset of training images instead of the whole training set.
Number of training images is varied from 10 to 70 in increments of 10 s.
The results are reported separately for various numbers of training images.
Firstly, a training protocol determines the fuzzy rules, according to features computed from a subset of training images taken from the SFU Laboratory dataset.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com