Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task

In this paper, we run two methods of explanation, namely LIME and Grad-CAM,\non a convolutional neural network trained to label images with the LEGO bricks\nthat are visible in them. We evaluate them on two criteria, the improvement of\nthe network's core performance and the trust they are able to generate for\nusers of the system. We find that in general, Grad-CAM seems to outperform LIME\non this specific task: it yields more detailed insight from the point of view\nof core performance and 80\\% of respondents asked to choose between them when\nit comes to the trust they inspire in the model choose Grad-CAM. However, we\nalso posit that it is more useful to employ these two methods together, as the\ninsights they yield are complementary.\n