Spatial Distribution Analysis of Images in GAN Training with Small Datasets

• Lucas F. Buzuti Centro Universitário FEI
• Tatiany M. Heiderich Centro Universitário FEI
• Carlos E. Thomaz Centro Universitário FEI

DOI: https://doi.org/10.5753/eniac.2022.227413

Abstract

Generative Adversarial Networks (GANs) are being increasingly used to artificially generate various types of data. Their training requires a sufficiently large dataset and becomes a challenge with small datasets. Recent works have proposed new approaches to training GANs with small samples. In this paper, we analyze the spatial distribution of the real and synthetic data of small datasets, building subspaces randomly, but varying their level of sparsing. To vary the level of sparsing, we propose the algorithm named k-Sparsest Sample. Our results show that small sets with the sparsest spatial distribution are able to generate data with high diversity.

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