Generative Adverserial Networks (GAN) have demonstrated significant success in learning the distribution of the data in an unsupervised fashion . As such they have been applied in many different settings, as pre-trainer for applications with little supervised data, anomaly detection , as well as for generating new datapoints and possibly augmenting the dataset, active learning etc. The quality of the generated data of GAN models for many types of data is typically subjectively assessed. Natural images generated by GAN models tend to be much sharper than other generative models and tend to incorporate many existing feature from the training data. However, since these models typically lack the contextual understanding of the data the generated images are still perceived with low level of ‘realness’.Subjective perception measurements are, however, difficult to incorporate in the training process of such generative models.
In this project we propose to build on top of advances in psythometics on subjective perception of stimuli  and advances in generative models in deep learning to develop a solution for generation of natural images that can benefit from feedback on subjective perception of ‘realness’ in the generated images.