Visual Explanations via Iterated Integrated Attributions
Oren Barkan Yehonatan Elisha Yuval Asher Amit Eshel Noam Koenigstein
We introduce Iterated Integrated Attributions (IIA) - a generic method for
explaining the predictions of vision models. IIA employs iterative integration
across the input image, the internal representations generated by the model,
and their gradients, yielding precise and focused explanation maps. We
demonstrate the effectiveness of IIA through comprehensive evaluations across
various tasks, datasets, and network architectures. Our results showcase that
IIA produces accurate explanation maps, outperforming other state-of-the-art
explanation techniques.