Evaluating the performance of the LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task
This work addresses the problem of evaluating explanation methods for multi-label image classification in a specific LEGO domain, which is incremental as it applies existing methods to a new dataset.
The paper evaluated LIME and Grad-CAM explanation methods on a LEGO multi-label image classification task, finding that Grad-CAM outperformed LIME in core performance and user trust, with 80% of respondents preferring it.
In this paper, we run two methods of explanation, namely LIME and Grad-CAM, on a convolutional neural network trained to label images with the LEGO bricks that are visible in them. We evaluate them on two criteria, the improvement of the network's core performance and the trust they are able to generate for users of the system. We find that in general, Grad-CAM seems to outperform LIME on this specific task: it yields more detailed insight from the point of view of core performance and 80\% of respondents asked to choose between them when it comes to the trust they inspire in the model choose Grad-CAM. However, we also posit that it is more useful to employ these two methods together, as the insights they yield are complementary.