A Generic Approach to Extend Interpretability of Deep Networks

Abstract

The recent advent of machine learning as a transforming technology has sparked fears about human inability to comprehend the rational of gradually more complex approaches. Interpretable Machine Learning (IML) was triggered by such concerns, with the purpose of enabling different actors to grasp the application scenarios, including trustworthiness and decision support in highly regulated sectors as those related to health and public services. YOLO (You Only Look Once) models, as other deep Convolutional Neural Network (CNN) approaches, have recently shown remarkable performance in several tasks dealing with object detection. However, interpretability of these models is still an open issue. Therefore, in this work we extend the LIME (Local Interpretable Model-agnostic Explanations) framework to be used with YOLO models. The main contribution is a public add-on to LIME that can effectively improve YOLO interpretability. Results on complex images show the potential improvement.