As the deployment of computer vision technology becomes increasingly common in applications of consequence such as medicine or science, the need for explanations of the system output has become a focus of great concern. Unfortunately, many state-of-the-art computer vision models are opaque, making their use challenging from an explanation standpoint, and current approaches to explaining these opaque models have stark limitations and have been the subject of serious criticism. In contrast, Explainable Boosting Machines (EBMs) are a class of models that are easy to interpret and achieve performance on par with the very best-performing models, however, to date EBMs have been limited solely to tabular data. Driven by the pressing need for interpretable models in science, we propose the use of 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, and, in doing so, demonstrate EBMs for image data for the first time. To tabularize the image data we employ Gabor Wavelet Transform-based techniques that preserve the spatial structure of the data. We show that our approach provides better explanations than other state-of-the-art explainability methods for images.

翻译：随着计算机视觉技术在医学、科学等影响深远领域的应用日益普及，对系统输出可解释性的需求已成为备受关注的重点。然而，众多前沿计算机视觉模型具有黑箱特性，导致其从解释角度难以使用，且当前解释这些黑箱模型的方法存在明显局限性并受到严重批评。相比之下，可解释性提升机（EBM）是一类易于解释且性能与顶级模型相媲美的模型，但迄今为止EBM仅局限于表格数据。受科学领域对可解释模型迫切需求的驱动，我们提出将EBM用于科学图像数据。受量子技术发展所依托的重要应用启发，我们将EBM应用于冷原子孤子图像数据，并首次展示了EBM在图像数据中的应用。为将图像数据表格化，我们采用基于Gabor小波变换的技术，该技术保留了数据的空间结构。研究表明，我们的方法相比其他前沿图像可解释性方法能提供更优的解释。