ML-CookGAN: Multi-Label Generative Adversarial Network for Food Image Generation

Abstract

Generating food images from recipe and ingredient information can be applied to many tasks such as food recommendation, recipe development, and health management. For the characteristics of food images, this paper proposes ML-CookGAN, a novel CGAN. This network enables the generation of food images based on recipe and ingredient labels. The generator of ML-CookGAN, Multi-Label Fusion Generator, converts recipe and ingredient labels into different granularity features and generates corresponding food images. The discriminator of ML-CookGAN, Multi-Branch Discriminator, implements discrimination and classification with a multi-branch structure. In addition, we propose two training strategies, Region-Wise Pooling and Image Style Distillation, to better the network performance. Region-Wise Pooling handles region-wise features with the discriminator. Image Style Distillation aims at extracting image latent features to assist image generation by an unsupervised method. The experiments conducted on VIREO Food-172 databases validate the proposed method to generate high-quality Chinese food images. And Region-Wise Pooling and Image Style Distillation are proven to enhance the diversity and realism of generated food images.