Research Article
A Food Dish Image Generation Framework Based on Progressive Growing GANs
@INPROCEEDINGS{10.1007/978-3-030-30146-0_22, author={Su Wang and Honghao Gao and Yonghua Zhu and Weilin Zhang and Yihai Chen}, title={A Food Dish Image Generation Framework Based on Progressive Growing GANs}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 15th EAI International Conference, CollaborateCom 2019, London, UK, August 19-22, 2019, Proceedings}, proceedings_a={COLLABORATECOM}, year={2019}, month={8}, keywords={GANs Food dish image synthesis Food dataset}, doi={10.1007/978-3-030-30146-0_22} }
- Su Wang
Honghao Gao
Yonghua Zhu
Weilin Zhang
Yihai Chen
Year: 2019
A Food Dish Image Generation Framework Based on Progressive Growing GANs
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-30146-0_22
Abstract
The generative adversarial networks (GANs) have demonstrated the ability to synthesize realistic images. However, there are few researches applying GANs into the field of food image synthesis. In this paper, we propose an extension to GANs for generating more realistic food dish images with rich detail, which adds a food condition that contains taste and other information. That makes the model generate images with rich details. To improve the quality of the generated image, the taste information condition is added to each stage of the generator and discriminator. First, the model learns embedding conditions of food information, including ingredients, cooking methods, tastes and cuisines. Secondly, the training model grows progressively, and the model learns details increasingly during the training process, which allows the model to generate images with rich details. To demonstrate the effectiveness of our proposed model, we collect a dataset called Food-121, which includes the names of the food, ingredients, cooking methods, tastes, and cuisines. The results of experiment show that our model can produce complex details of food dish image and obtain high inception score on the Food-121 dataset compared with other models.