  Multilabel conditional image generation is a challenging problem in computer
vision. In this work we propose Multi-ingredient Pizza Generator (MPG), a
conditional Generative Neural Network (GAN) framework for synthesizing
multilabel images. We design MPG based on a state-of-the-art GAN structure
called StyleGAN2, in which we develop a new conditioning technique by enforcing
intermediate feature maps to learn scalewise label information. Because of the
complex nature of the multilabel image generation problem, we also regularize
synthetic image by predicting the corresponding ingredients as well as
encourage the discriminator to distinguish between matched image and mismatched
image. To verify the efficacy of MPG, we test it on Pizza10, which is a
carefully annotated multi-ingredient pizza image dataset. MPG can successfully
generate photo-realist pizza images with desired ingredients. The framework can
be easily extend to other multilabel image generation scenarios.
