Cycle-Generative Adversarial Network for Generating a Pseudo Realistic Food Dataset Using RGB and Depth Images

Abstract

Constructing a food dataset is time and effort consuming due to the requirement for covering the feature variations of food samples. Additionally, a large dataset is needed for training neural networks. Generative adversarial networks (GANs) are a recently developed technique to learn deep representations without extensively annotated training data. They can be used in several applications, including generating food datasets. This paper advocates the use of Cycle-GAN to generate a large pseudo-realistic food dataset based on a large number of simulated images and a small number of real images in comparison to traditional techniques. A single depth camera in three different angles and a turntable are arranged to capture real RGB-D images of food samples. 3D modeling software is used to generate simulated images using the same configuration of captured real images. Results showed that Cycle-GAN realistic style transfer on simulated food objects is achievable, and that it can be an efficient tool to minimize real image capturing efforts.