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Chase Walker

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3 papers

2 author rows

ICLR Conference 2025 Conference Paper

Metric-Driven Attributions for Vision Transformers

Chase Walker
Sumit Kumar Jha 0001
Rickard Ewetz

Attribution algorithms explain computer vision models by attributing the model response to pixels within the input. Existing attribution methods generate explanations by combining transformations of internal model representations such as class activation maps, gradients, attention, or relevance scores. The effectiveness of an attribution map is measured using attribution quality metrics. This leads us to pose the following question: if attribution methods are assessed using attribution quality metrics, why are the metrics not used to generate the attributions? In response to this question, we propose a Metric-Driven Attribution for explaining Vision Transformers (ViT) called MDA. Guided by attribution quality metrics, the method creates attribution maps by performing patch order and patch magnitude optimization across all patch tokens. The first step orders the patches in terms of importance and the second step assigns the magnitude to each patch while preserving the patch order. Moreover, MDA can provide a smooth trade-off between sparse and dense attributions by modifying the optimization objective. Experimental evaluation demonstrates the proposed MDA method outperforms $7$ existing ViT attribution methods by an average of $12\%$ across $12$ attribution metrics on the ImageNet dataset for the ViT-base $16 \times 16$, ViT-tiny $16 \times 16$, and ViT-base $32 \times 32$ models. Code is publicly available at https://github.com/chasewalker26/MDA-Metric-Driven-Attributions-for-ViT.

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IJCAI Conference 2024 Conference Paper

Attribution Quality Metrics with Magnitude Alignment

Chase Walker
Dominic Simon
Kenny Chen
Rickard Ewetz

Attribution algorithms play an instrumental role in human interpretation of AI models. The methods measure the importance of the input features to the model output decision, which can be displayed as an attribution map for image classifiers. Perturbation tests are the state-of-the-art approach to evaluate the quality of an attribution map. Unfortunately, we observe that perturbation tests fail to consider attribution magnitude, which translates into inconsistent quality scores. In this paper, we propose Magnitude Aligned Scoring (MAS), a new attribution quality metric that measures the alignment between the magnitude of the attributions and the model response. In particular, the metric accounts for both the relative ordering and the magnitude of the pixels within an attribution. In the experimental evaluation, we compare the MAS metric with existing metrics across a wide range of models, datasets, attributions, and evaluations. The results demonstrate that the MAS metric is 4x more sensitive to attribution changes, 2x more consistent, and 1. 6x more invariant to baseline modifications. Our code and the referenced appendix are publicly available via https: //github. com/chasewalker26/Magnitude-Aligned-Scoring.

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AAAI Conference 2024 Conference Paper

Integrated Decision Gradients: Compute Your Attributions Where the Model Makes Its Decision

Chase Walker
Sumit Jha
Kenny Chen
Rickard Ewetz

Attribution algorithms are frequently employed to explain the decisions of neural network models. Integrated Gradients (IG) is an influential attribution method due to its strong axiomatic foundation. The algorithm is based on integrating the gradients along a path from a reference image to the input image. Unfortunately, it can be observed that gradients computed from regions where the output logit changes minimally along the path provide poor explanations for the model decision, which is called the saturation effect problem. In this paper, we propose an attribution algorithm called integrated decision gradients (IDG). The algorithm focuses on integrating gradients from the region of the path where the model makes its decision, i.e., the portion of the path where the output logit rapidly transitions from zero to its final value. This is practically realized by scaling each gradient by the derivative of the output logit with respect to the path. The algorithm thereby provides a principled solution to the saturation problem. Additionally, we minimize the errors within the Riemann sum approximation of the path integral by utilizing non-uniform subdivisions determined by adaptive sampling. In the evaluation on ImageNet, it is demonstrated that IDG outperforms IG, Left-IG, Guided IG, and adversarial gradient integration both qualitatively and quantitatively using standard insertion and deletion metrics across three common models.

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