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Puzzle solving is a combinatorial challenge due to the difficulty of matchingadjacent pieces. Instead, we infer a mental image from all pieces, which agiven piece can then be matched against avoiding the combinatorial explosion.Exploiting advancements in Generative Adversarial methods, we learn how toreconstruct the image given a set of unordered pieces, allowing the model tolearn a joint embedding space to match an encoding of each piece to the croppedlayer of the generator. Therefore we frame the problem as a R@1 retrieval task,and then solve the linear assignment using differentiable Hungarian attention,making the process end-to-end. In doing so our model is puzzle size agnostic,in contrast to prior deep learning methods which are single size. We evaluateon two new large-scale datasets, where our model is on par with deep learningmethods, while generalizing to multiple puzzle sizes.

GANzzle: Reframing jigsaw puzzle solving as a retrieval task using a generative mental image