Authors:
Emerson Vilar de Oliveira
;
Dunfrey Aragão
and
Luiz Gonçalves
Affiliation:
Universidade Federal do Rio Grande do Norte, Av. Salgado Filho, 3000, Campus Universitário, 59.078-970, Natal, Brazil
Keyword(s):
Autoencoder, Stacked Autoencoder, Latent Space, Image Classification, Feature Extraction.
Abstract:
In the field of computer vision, image classification has been aiding in the understanding and labeling of images. Machine learning and artificial intelligence algorithms, especially artificial neural networks, are widely used tools for this task. In this work, we present the Expanded Latent space Autoencoder (ELSA). The ELSA network consists of more than one autoencoder in its internal structure, concatenating their latent spaces and constructing an expanded latent space. The expanded latent space aims to extract more information from input data. Thus, this expanded latent space can be used by other networks for general tasks such as prediction and classification. To evaluate these capabilities, we created an image classification network for the FashionM-NIST and MNIST datasets, achieving 99.97 and 99.98 accuracy for the test dataset. The classifier trained with the expanded latent space dataset outperforms some models in public benchmarks.