Semantic Preserving Generative Adversarial Models

Shahar Harel, Meir Maor †, Amir Ronen ‡ SparkBeyond L TD Israel Abstract We introduce generative adversarial models in which the discriminator is replaced by a calibrated (non-differentiable) classifier repeatedly enhanced by domain relevant features. The role of the classifier is to prove that the actual and generated data differ over a controlled semantic space. We demonstrate that such models have the ability to generate objects with strong guarantees on their properties in a wide range of domains. They require less data than ordinary GANs, provide natural stopping conditions, uncover important properties of the data, and enhance transfer learning. Our techniques can be combined with standard generative models. We demonstrate the usefulness of our approach by applying it to several unrelated domains: generating good locations for cellular antennae, molecule generation preserving key chemical properties, and generating and extrapolating lines from very few data points. Intriguing open problems are presented as well. 1 Introduction Generative adversarial networks (GANs) (Goodfellow et al. 2014) achieved many impressive results. Recent literature surveys as well as a large code repository can be found at (Creswell et al. 2017; Kurach et al. 2018; Hindupur). Arguably however, most of these results were obtained for generation of images, text, and videos. First, humans have very good judgment of the quality of the generated objects and hence can fine-tune the generative model until it is satisfactory. Second, there exists a huge amount of available data that can be used for model training. This is unlikely to be the case in a wide range of important domains (e.g.