An image generator based on neural networks in GPU

Abstract

Existing image databases contain a few diversity of images. Likewise, there is no specific image base available in other situations, leading to the need to undertake additional efforts in capturing images and creating datasets. Many of these datasets contain only a single object in each image, but often the scenario in which projects must operate in production requires several objects per image. Thus, it is necessary to expand original datasets into more complex ones with specific combinations to achieve the goal of the application. This work proposes a technique for image generation to extend an initial dataset. It has been designed generically to work with various images and create a data set from some initial images. The generated set of images is used in a distributed environment. It is possible to perform image generation in this environment, producing datasets with specific images to work in certain applications. The generation of images consists of two methods: generation by deformation and generation by a neural network. With the proposed methods, this work sought to bring as main contributions the specification and implementation of an image generating component so that it is possible to easily integrate it with possible heterogeneous devices capable of parallel computing, such as General Purpose Graphics Processing Unit (GPGPU). In comparison with the existing methods to the proposed one, this one proposes to use the image generator enlarging an initial image bank with the combination of two methods. Some experiments are presented doing generation with handwritten digits to validate the proposed approach. The generator was designed with CUDA and GPU-optimized libraries as TensorFlow-specific modules. The results obtained can optimize the integration process with the simulation of possible stimuli choices, avoiding problems in the generation of image phase tests.