Towards credible visual model interpretation with path attribution

• URL: http://arxiv.org/abs/2305.14395v1
• Date: Tue, 23 May 2023 06:23:08 GMT
• Title: Towards credible visual model interpretation with path attribution
• Authors: Naveed Akhtar, Muhammad A. A. K. Jalwana
• Abstract summary: path attribution framework stands out among the post-hoc model interpretation tools due to its axiomatic nature. Recent developments show that this framework can still suffer from counter-intuitive results. We devise a scheme to preclude the conditions in which visual model interpretation can invalidate the axiomatic properties of path attribution.
• Score: 24.86176236641865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Originally inspired by game-theory, path attribution framework stands out among the post-hoc model interpretation tools due to its axiomatic nature. However, recent developments show that this framework can still suffer from counter-intuitive results. Moreover, specifically for deep visual models, the existing path-based methods also fall short on conforming to the original intuitions that are the basis of the claimed axiomatic properties of this framework. We address these problems with a systematic investigation, and pinpoint the conditions in which the counter-intuitive results can be avoided for deep visual model interpretation with the path attribution strategy. We also devise a scheme to preclude the conditions in which visual model interpretation can invalidate the axiomatic properties of path attribution. These insights are combined into a method that enables reliable visual model interpretation. Our findings are establish empirically with multiple datasets, models and evaluation metrics. Extensive experiments show a consistent performance gain of our method over the baselines.

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