Obtaining Faithful Interpretations from Compositional Neural Networks
Table 6: Faithfulness scores on NLVR2 using a negative IOU threshold of 10−8 and example-wise averaging.
['[BOLD] Model', '[BOLD] Performance (Accuracy)', '[BOLD] Overall Faithful.(↑) Prec.', '[BOLD] Overall Faithful.(↑) Rec.', '[BOLD] Overall Faithful.(↑) F1', 'Module-wise Faithfulness(↑) find', 'Module-wise Faithfulness(↑) filter', 'Module-wise Faithfulness(↑) with-relation', 'Module-wise Faithfulness(↑) relocate']
We also noticed qualitatively that the metric can penalize modules that assign high probability to proposed bounding boxes that have a relatively high IOU that does not quite pass the IOU threshold of 0.5. In such cases, while it may not make sense to give the model credit in its recall score, it also may not make sense to penalize the model in its precision score. Consequently, we also performed an evaluation in which for the precision calculation we set a separate “negative” IOU threshold of 10−8 (effectively 0) and only penalized modules for high probabilities assigned to proposed boxes whose IOU is below this threshold.