r/MachineLearning - [R] OpenGAN: Open Set Generative Adversarial Networks

Abstract: Many existing conditional Generative Adversarial Networks (cGANs) are limited to conditioning on pre-defined and fixed class-level semantic labels or attributes. We propose an open set GAN architecture (OpenGAN) that is conditioned per-input sample with a feature embedding drawn from a metric space. Using a state-of-the-art metric learning model that encodes both class- level and fine-grained semantic information, we are able to generate samples that are semantically similar to a given source image. The semantic information extracted by the metric learning model transfers to out-of- distribution novel classes, allowing the generative model to produce samples that are outside of the training distribution. We show that our proposed method is able to generate 256$\times$256 resolution images from novel classes that are of similar visual quality to those from the training classes.