Food image generation using a large amount of food images with conditional GAN
Yoshifumi Ito, Wataru Shimoda, Keiji Yanai · 2018
Recently, image generation by Deep Convolutional Neural Network has been studied widely by many researchers. In this paper, we describe CNN-based image generation on food images. Especially, we focus on image generation using conditional Generative Adversarial Network (cGAN) with a large-scale dataset. In the experiments, we trained cGAN with a "ramen" image dataset and a recipe image dataset. For "ramen"GAN, we added a dish plate discriminator to make the shape of dishes rounder in generated images. For "recipe"GAN, we generated dish images from cooking ingredients, and tried image-based recipe search with generated images for the recipe database.