Deja vu from the SVM Era: Example-based Explanations with Outlier Detection
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Understanding the features that contributed to a prediction is important for high-stake tasks. In this work, we revisit the idea of a student network to provide an example-based explanation for its prediction in two forms: i) identify top-k most relevant prototype examples and ii) show evidence of similarity between the prediction sample and each of the top-k prototypes. We compare the prediction performance and the explanation performance for the second type of explanation with the teacher network. In addition, we evaluate the outlier detection performance of the network. We show that using prototype-based students beyond similarity kernels deliver meaningful explanations and promising outlier detection results, without compromising on classification accuracy.
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