Author details

both axes. The result shows a soft transition at each change from the selected class with different features related to the position of the x, y values (see Figure 5).

Sampled digits in continuous x, y values from the latent space interpolation. The conditioner of the vector changes only on the dimension that represents each class: 9, 5, and 2, respectively, top to bottom.

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information in a loop.

learning.

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Figure 5.

4. Conclusions

We were able to generate the image of the requested number; the training took up 30,000 epochs with a learning error rate no lower than 1% computed from the MSE (Eq. (1)) of the neural network training stage. The generation was an interpolation of a continuous value sampled from the latent space. The latent space given by the reference values showed a correlation in both dimensions, which results in a problem when sampling from outside the distribution of the latent space. The generated information is incomplete or incorrect. In addition, the abstraction of the data in two dimensions makes the sampling result still blurry as the VAEs do. If it uses more dimensions to abstract the information, the output results in black gaps inside the distributions of the latent space. When sampling from the empty space, the generation results with no information or in incomplete generated images. A possible solution for this completion of the information or latent space management could be the training of a K-autoencoder [18] to learn the sample space and generate a secondary latent space based on the incomplete dimensions to get only valid data that is mapped from control space. This will increase complexity, but it still needs to be tested. To improve generation, the input update should stop after covering the visible (x, y) axis of the latent space. This needs to be done because, since we are trying to represent the encoded information and the resembled images are projected into only two-dimensional space, the projection of the inputs keeps updating, and so the learning keeps updating; it is necessary to stop changing the inputs. To keep conditioning different aspects of a dataset, we need those features already labeled as in supervised learning. Otherwise, the network tries to cluster the

This technique of keeping the input in a fixed learned state helps to increase the quality of the generated images. The variation of the classes in the dataset is another problem with the dimensionality reduction. Reducing the variation or increasing the dimensionality representation, the quality of the generated data also increases, and it reduces the learning error of the network. The model also was able to learn without the labels, but it was slower, and the error rate kept a higher threshold of

This method is a simplified version of a neural network generator of two networks reduced into only one network architecture, which can learn to generate interpolations from given samples like GAN and VAE architecture do. Both models

are based on two neural networks, and their parameters are optimized with

Omar López-Rincón\* and Oleg Starostenko\* Department of Computing, Electronics and Mechatronics, Universidad de las Américas Puebla, Cholula, Puebla, Mexico

\*Address all correspondence to: omar.lopezrn@udlap.mx and oleg.starostenko@udlap.mx

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
