2018 · 17 citations · 6 references
AgricultureFood ScienceComputer VisionFood EngineeringFoodways StudiesFood Composition DatabasesFood Representation In MediaCross-category TransferFood ImageryFood Representation
This paper describes "food image transformation" based on a conditional cycleGAN[A3] (cCycleGAN) with a large-scale food image data collected from the Twitter stream. A cCycleGAN is an extension of CycleGAN, which enables "food category transfer" among 10 types of foods and retain the shape of a given food. We experimentally show that 200 and 30,000 food images with the cCycleGAN enable a very natural food category transfer among 10 types of typical Japanese foods: ramen noodle, curry rice, fried rice, beef rice bowl, chilled noodle, spaghetti with meat source, white rice, eel bowl, and fried noodle.
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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
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