Generating Images Instead of Retrieving Them: Relevance Feedback on Generative Adversarial Networks

Finding images matching a user's intention has been largely based on matching a representation of the user's information needs with an existing collection of images. For example, using an example image or a written query to express the information need and retrieving images that share similarities with the query or example image. However, such an approach is limited to retrieving only images that already exist in the underlying collection. Here, we present a methodology for generating images matching the user intention instead of retrieving them. The methodology utilizes a relevance feedback loop between a user and generative adversarial neural networks (GANs). GANs can generate novel photorealistic images which are initially not present in the underlying collection, but generated in response to user feedback. We report experiments (N=29) where participants generate images using four different domains and various search goals with textual and image targets. The results show that the generated images match the tasks and outperform images selected as baselines from a fixed image collection. Our results demonstrate that generating new information can be more useful for users than retrieving it from a collection of existing information.