Adversarial Placement Vector Learning

Automated jigsaw puzzle solving is a challenging problem with numerous scientific applications. We explore whether a Generative Adversarial Network (GAN) can output jigsaw piece placements. State-of-the-art GANs for image-to-image translation cannot solve the jigsaw problem in an exact fashion. Instead of learning image-to-image mappings, we propose a novel piece-to-location mapping problem and present a trainable generative model for producing output that can be interpreted as the placement of jigsaw pieces. This represents a first step in developing a complete learning-based generative model for piece-to-location mappings. We introduce four new evaluation measures for the quality of output locations and show that locations generated by our model perform favorably.