As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern. Driven by the pressing need for interpretable models in science, we propose the use of Explainable Boosting Machines (EBMs) for scientific image data. Inspired by an important application underpinning the development of quantum technologies, we apply EBMs to cold-atom soliton image data tabularized using Gabor Wavelet Transform-based techniques that preserve the spatial structure of the data. In doing so, we demonstrate the use of EBMs for image data for the first time and show that our approach provides explanations that are consistent with human intuition about the data.

翻译：随着计算机视觉技术在科学领域的部署日益普遍，对其系统及输出结果的可解释性需求已成为关注焦点。受科学领域对可解释模型紧迫需求的驱动，我们提出将可解释增强机器（EBMs）应用于科学图像数据。受量子技术发展关键应用的启发，我们基于Gabor小波变换技术将冷原子孤子图像数据表格化，该方法能有效保留数据的空间结构。通过这一实践，我们首次验证了EBMs在图像数据中的应用，并证明该方法能提供与人类对数据直觉认知一致的解释。