论文标题
ganzzle：使用生成的心理图像将拼图拼图求解作为检索任务
GANzzle: Reframing jigsaw puzzle solving as a retrieval task using a generative mental image
论文作者
论文摘要
由于难以匹配相邻的碎片，因此解决难题是一个组合挑战。取而代之的是，我们从所有碎片中推断出一个心理图像，然后可以将其与避免爆炸的组合相匹配。利用生成对抗方法的进步，我们学习如何重建给定一组无序的零件的图像，从而使模型可以学习一个关节嵌入空间，以将每个零件的编码与生成器的裁剪层匹配。因此，我们将问题作为R@1检索任务将其构架，然后使用可区分的匈牙利注意力解决线性分配，从而使过程端到端。这样一来，我们的模型是拼图尺寸不可知论，与先前的深度学习方法相比。我们在两个新的大规模数据集上进行了评估，其中我们的模型与深度学习方法相当，同时推广到多个拼图大小。
Puzzle solving is a combinatorial challenge due to the difficulty of matching adjacent pieces. Instead, we infer a mental image from all pieces, which a given piece can then be matched against avoiding the combinatorial explosion. Exploiting advancements in Generative Adversarial methods, we learn how to reconstruct the image given a set of unordered pieces, allowing the model to learn a joint embedding space to match an encoding of each piece to the cropped layer 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 evaluate on two new large-scale datasets, where our model is on par with deep learning methods, while generalizing to multiple puzzle sizes.
