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arXiv · 2301.07002

Opti-CAM: Optimizing saliency maps for interpretability

Hanwei Zhang·Felipe Torres·Ronan Sicre·Yannis Avrithis·Stephane Ayache
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Abstract

Methods based on class activation maps (CAM) provide a simple mechanism to interpret predictions of convolutional neural networks by using linear combinations of feature maps as saliency maps. By contrast, masking-based methods optimize a saliency map directly in the image space or learn it by training another network on additional data. In this work we introduce Opti-CAM, combining ideas from CAM-based and masking-based approaches. Our saliency map is a linear combination of feature maps, where weights are optimized per image such that the logit of the masked image for a given class is maximized. We also fix a fundamental flaw in two of the most common evaluation metrics of attribution methods. On several datasets, Opti-CAM largely outperforms other CAM-based approaches according to the most relevant classification metrics. We provide empirical evidence supporting that localization and classifier interpretability are not necessarily aligned.

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Hanwei Zhang, Felipe Torres, Ronan Sicre, Yannis Avrithis, Stephane Ayache. 2023-01-17. Opti-CAM: Optimizing saliency maps for interpretability. https://arxiv.org/abs/2301.07002

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