Towards Credible Visual Model Interpretation with Path Attribution

Naveed Akhtar, Mohammad A.A.K. Jalwana

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

With its inspirational roots in game-theory, path attribution framework stands out among the post-hoc model interpretation techniques due to its axiomatic nature. However, recent developments show that despite being axiomatic, path attribution methods can compute counter-intuitive feature attributions. Not only that, for deep visual models, the methods may also not conform to the original game-theoretic intuitions that are the basis of their axiomatic nature. To address these issues, we perform a systematic investigation of the path attribution framework. We first pinpoint the conditions in which the counter-intuitive attributions of deep visual models can be avoided under this framework. Then, we identify a mechanism of integrating the attributions over the paths such that they computationally conform to the original insights of game-theory. These insights are eventually combined into a method, which provides intuitive and reliable feature attributions. We also establish the findings empirically by evaluating the method on multiple datasets, models and evaluation metrics. Extensive experiments show a consistent quantitative and qualitative gain in the results over the baselines.

Original languageEnglish
Pages (from-to)439-457
Number of pages19
JournalProceedings of Machine Learning Research
Volume202
Publication statusPublished - 2023
Event40th International Conference on Machine Learning - Honolulu, United States
Duration: 23 Jul 202329 Jul 2023

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