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Nonparametric Guidance of Autoencoder Representations using Label Information

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Figure

Figure 1: We explore the benefit of the NPGA on the oil data through adjusting the relative con-tributions of the autoencoder, logistic regressor and GP costs in the hybrid objective bymodifying α and β
Figure 2: A sample of filters learned on the CIFAR 10 data set. This model achieved a test accuracyof 65.71%
Table 1: Results on CIFAR 10 for various training strategies, varying the nonparametric guidanceα
Figure 3: Visualisations of the NORB training (top) and test (bottom) data latent space representa-tions in the NPGA, corresponding to class (first column), elevation (second column), andlighting (third column)
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