论文标题
通过基于路径计划的算法来解释图像增强黑框方法
Explaining Image Enhancement Black-Box Methods through a Path Planning Based Algorithm
论文作者
论文摘要
如今，图像到图像翻译方法是增强自然图像的艺术状态。即使它们通常在准确性方面表现出高度的表现，它们也经常受到一些局限性的限制，例如人工制品的产生以及对高分辨率的可扩展性。此外，他们的主要缺点是完全黑框的方法，它不允许为最终用户提供有关应用程序所应用的任何见解。在本文中，我们提出了一种路径计划算法，该算法对通过最先进的增强方法产生的输出进行了分步说明，并克服了黑盒限制。该算法（称为Exie）使用A*算法的变体通过应用等效的增强运算符序列来模仿另一种方法的增强过程。我们应用了Exie来解释在五千数据集上训练的几种最先进模型的输出，从而获得了增强运算符的序列，能够在性能方面产生非常相似的结果，并克服了最佳性能算法的巨大可解释性的巨大限制。
Nowadays, image-to-image translation methods, are the state of the art for the enhancement of natural images. Even if they usually show high performance in terms of accuracy, they often suffer from several limitations such as the generation of artifacts and the scalability to high resolutions. Moreover, their main drawback is the completely black-box approach that does not allow to provide the final user with any insight about the enhancement processes applied. In this paper we present a path planning algorithm which provides a step-by-step explanation of the output produced by state of the art enhancement methods, overcoming black-box limitation. This algorithm, called eXIE, uses a variant of the A* algorithm to emulate the enhancement process of another method through the application of an equivalent sequence of enhancing operators. We applied eXIE to explain the output of several state-of-the-art models trained on the Five-K dataset, obtaining sequences of enhancing operators able to produce very similar results in terms of performance and overcoming the huge limitation of poor interpretability of the best performing algorithms.
