[1906.02839] How to make a pizza: Learning a compositional layer-based GAN model

A food recipe is an ordered set of instructions for preparing a particular dish. From a visual perspective, every instruction step can be seen as a way to change the visual appearance of the dish by adding extra objects (e.g., adding an ingredient) or changing the appearance of the existing ones (e.g., cooking the dish). In this paper, we aim to teach a machine how to make a pizza by building a generative model that mirrors this step-by-step procedure. To do so, we learn composable module operations which are able to either add or remove a particular ingredient. Each operator is designed as a Generative Adversarial Network (GAN). Given only weak image-level supervision, the operators are trained to generate a visual layer that needs to be added to or removed from the existing image. The proposed model is able to decompose an image into an ordered sequence of layers by applying sequentially in the right order the corresponding removing modules. Experimental results on synthetic and real

10 mentions: @quantombone@WonderMicky@ryanfb@_mxochicale@Kazk1018@xayma@FelixEVargas@trinity_site
Keywords: gan
Date: 2019/06/10 03:47

Referring Tweets

@_mxochicale PizzaGAN 🍕 arXiv: https://t.co/ll7QtksJNW Website: https://t.co/Q7ADdl7rlW https://t.co/ZuJm6J73Ql
@quantombone How to make a pizza: Learning a compositional layer-based GAN model. Or “MIT’s AI learns to Become Pizza Guru. All pizza design will soon be automated. ” https://t.co/nXU5qSoEBp #gan #ai #computervision https://t.co/TbItb8icqX

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