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.
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Acknowledgments
This work is supported by the National Key Research and Development Plan of China under Grant No. 2017YFD0400101, the Natural Science Foundation of Shanghai under Grant No. 16ZR1411200, and CERNET Innovation Project under Grant No. NGII20170513.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, S., Gao, H., Zhu, Y., Zhang, W., Chen, Y. (2019). A Food Dish Image Generation Framework Based on Progressive Growing GANs. In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_22
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