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Peiyu Yang

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AAAI Conference 2023 Conference Paper

Local Path Integration for Attribution

  • Peiyu Yang
  • Naveed Akhtar
  • Zeyi Wen
  • Ajmal Mian

Path attribution methods are a popular tool to interpret a visual model's prediction on an input. They integrate model gradients for the input features over a path defined between the input and a reference, thereby satisfying certain desirable theoretical properties. However, their reliability hinges on the choice of the reference. Moreover, they do not exhibit weak dependence on the input, which leads to counter-intuitive feature attribution mapping. We show that path-based attribution can account for the weak dependence property by choosing the reference from the local distribution of the input. We devise a method to identify the local input distribution and propose a technique to stochastically integrate the model gradients over the paths defined by the references sampled from that distribution. Our local path integration (LPI) method is found to consistently outperform existing path attribution techniques when evaluated on deep visual models. Contributing to the ongoing search of reliable evaluation metrics for the interpretation methods, we also introduce DiffID metric that uses the relative difference between insertion and deletion games to alleviate the distribution shift problem faced by existing metrics. Our code is available at https://github.com/ypeiyu/LPI.

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ICLR Conference 2023 Conference Paper

Re-calibrating Feature Attributions for Model Interpretation

  • Peiyu Yang
  • Naveed Akhtar
  • Zeyi Wen
  • Mubarak Shah
  • Ajmal Mian

The ability to interpret machine learning models is critical for high-stakes applications. Due to its desirable theoretical properties, path integration is a widely used scheme for feature attribution to interpret model predictions. However, the methods implementing this scheme currently rely on absolute attribution scores to eventually provide sensible interpretations. This not only contradicts the premise that the features with larger attribution scores are more relevant to the model prediction, but also conflicts with the theoretical settings for which the desirable properties of the attributions are proven. We address this by devising a method to first compute an appropriate reference for the path integration scheme. This reference further helps in identifying valid interpolation points on a desired integration path. The reference is computed in a gradient ascending direction on the model's loss surface, while the interpolations are performed by analyzing the model gradients and variations between the reference and the input. The eventual integration is effectively performed along a non-linear path. Our scheme can be incorporated into the existing integral-based attribution methods. We also devise an effective sampling and integration procedure that enables employing our scheme with multi-reference path integration efficiently. We achieve a marked performance boost for a range of integral-based attribution methods on both local and global evaluation metrics by enhancing them with our scheme. Our extensive results also show improved sensitivity, sanity preservation and model robustness with the proposed re-calibration of the attribution techniques with our method.

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