论文标题
十：双嵌入网络用于拼图拼图问题，边界侵蚀
TEN: Twin Embedding Networks for the Jigsaw Puzzle Problem with Eroded Boundaries
论文作者
论文摘要
本文介绍了基于CNN的新型编码器双嵌入网络（十），用于拼图拼图问题（JPP），该网络代表了在潜在嵌入空间中与其边界相对于其边界的拼图。将这种潜在表示与一个简单的距离度量相结合，我们证明了与各种经典方法相比，新提出的成对兼容性度量（CM）的精度提高了，对于具有侵蚀的瓷砖边界的降级难题。我们专注于我们的案例研究的问题实例，因为它是现实世界情景的适当测试。具体而言，对于所谓的1型和2型问题变体，我们证明了重建精度的提高高达8.5％和16.8％。此外，我们还证明，比典型的深神经网络（NN）模型平均而言，十个数量级要快，即它与经典方法一样快。在这方面，本文是为实用的，现实世界中的拼图样问题（经典方法和密集的计算复杂性（NN模型）（nn模型的强化计算复杂性）之间的差异（经典方法和密集的计算复杂性）之间的差距。
This paper introduces the novel CNN-based encoder Twin Embedding Network (TEN), for the jigsaw puzzle problem (JPP), which represents a puzzle piece with respect to its boundary in a latent embedding space. Combining this latent representation with a simple distance measure, we demonstrate improved accuracy levels of our newly proposed pairwise compatibility measure (CM), compared to that of various classical methods, for degraded puzzles with eroded tile boundaries. We focus on this problem instance for our case study, as it serves as an appropriate testbed for real-world scenarios. Specifically, we demonstrated an improvement of up to 8.5% and 16.8% in reconstruction accuracy, for so-called Type-1 and Type-2 problem variants, respectively. Furthermore, we also demonstrated that TEN is faster by a few orders of magnitude, on average, than a typical deep neural network (NN) model, i.e., it is as fast as the classical methods. In this regard, the paper makes a significant first attempt at bridging the gap between the relatively low accuracy (of classical methods and the intensive computational complexity (of NN models), for practical, real-world puzzle-like problems.
