Sanity Checks for Saliency Maps Sanity Checks for Saliency Maps
Paper summary **Idea:** With the growing use of visual explanation systems of machine learning models such as saliency maps, there needs to be a standardized method of verifying if a saliency method is correctly describing the underlying ML model. **Solution:** In this paper two Sanity Checks have been proposed to verify the accuracy and the faithfulness of a saliency method: * *Model parameter randomization test:* In this sanity check the outputs of a saliency method on a trained model is compared to that of the same method on an untrained randomly parameterized model. If these images are similar/identical then this saliency method does not correctly describe the model. In the course of this experiment it is found that certain methods such as the Guided BackProp are constant in their explanations despite alterations in the model. * *Data Randomization Test:* This method explores the relationship of saliency methods to data and their associated labels. In this test, the labels of the training data are randomized thus there should be no definite pattern describing the model (Since the model is as good as randomly guessing an output label). If there is a definite pattern, this shows that the saliency methods are independent of the underlying model/training data labels. In this test as well Guided BackProp did not fare well, implying this saliency method is as good as an edge detector as opposed to a ML explainer. Thus this paper makes a valid argument toward having standardized tests that an interpretation model must satisfy to be deemed accurate or faithful.
Sanity Checks for Saliency Maps
Julius Adebayo and Justin Gilmer and Michael Muelly and Ian Goodfellow and Moritz Hardt and Been Kim
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.CV, cs.LG, stat.ML


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