Enhancing Visualization and Explainability of Computer Vision Models with Local Interpretable Model-Agnostic Explanations (LIME)

It is important that humans understand why machine learning models behave the way they do, especially in the field of computer vision. Having methods for visualizing which regions of an image are responsible for classifying or detecting objects can be a very useful resource. One popular algorithm for doing so is Local Interpretable Model-agnostic Explanations (LIME). We introduce Sub-model Stabilized and Sub grid Superimposed LIME (SubLIME), a technique for enhancing the stability of LIME-based visualizations as well as increasing the resolution of those explanations using a superimposition technique. Demonstrations are shown on the MNIST handwritten digit data set as well as a real-world data set for object detection in overhead infrared imagery.