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.
@article{arxiv.2310.18585,
title = {Visual Explanations via Iterated Integrated Attributions},
author = {Oren Barkan and Yehonatan Elisha and Yuval Asher and Amit Eshel and Noam Koenigstein},
journal= {arXiv preprint arXiv:2310.18585},
year = {2023}
}