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H-Sets framework uncovers feature interactions in image classifiers

By PulseAugur Editorial · [2 sources] ·

Researchers have developed H-Sets, a new framework designed to uncover and attribute higher-order feature interactions within image classifiers. This method moves beyond analyzing individual features to understand how groups of features collectively influence a model's output. H-Sets utilizes input Hessians to detect interacting feature pairs and then merges them into coherent sets, employing a set-level extension of Integrated Directional Gradients for attribution. Evaluations on various models and datasets indicate that H-Sets produce more interpretable and faithful saliency maps compared to existing techniques. AI

IMPACT Enhances interpretability of image classifiers by revealing complex feature interactions, potentially improving model debugging and trust.

RANK_REASON Academic paper detailing a new method for feature attribution in image classifiers.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

H-Sets framework uncovers feature interactions in image classifiers

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Academic paper detailing a new method for feature attribution in image classifiers.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ayushi Mehrotra, Dipkamal Bhusal, Michael Clifford, Nidhi Rastogi ·

    H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers

    arXiv:2604.22045v1 Announce Type: new Abstract: Feature attribution methods explain the predictions of deep neural networks by assigning importance scores to individual input features. However, most existing methods focus solely on marginal effects, overlooking feature interactio…

  2. arXiv cs.CV TIER_1 English(EN) · Nidhi Rastogi ·

    H-Sets: Hessian-Guided Discovery of Set-Level Feature Interactions in Image Classifiers

    Feature attribution methods explain the predictions of deep neural networks by assigning importance scores to individual input features. However, most existing methods focus solely on marginal effects, overlooking feature interactions, where groups of features jointly influence m…

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