MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs
arXiv:2012.02821
Abstract
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.
References in corpus (14)
- Auto-Encoding Variational Bayes
- Conditional Generative Adversarial Nets
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- Spectral Normalization for Generative Adversarial Networks
- HuggingFace's Transformers: State-of-the-art Natural Language Processing
- Conditional Image Synthesis With Auxiliary Classifier GANs
- NICE: Non-linear Independent Components Estimation
- Which Training Methods for GANs do actually Converge?
- Density estimation using Real NVP
- Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients
- Controllable Text-to-Image Generation
- LoGANv2: Conditional Style-Based Logo Generation with Generative Adversarial Networks
- GILT: Generating Images from Long Text