Make it SING: Analyzing Semantic Invariants in Classifiers

• URL: http://arxiv.org/abs/2603.14610v2
• Date: Tue, 17 Mar 2026 07:33:45 GMT
• Title: Make it SING: Analyzing Semantic Invariants in Classifiers
• Abstract summary: We present a method that constructs equivalent images, with respect to the network, and assigns semantic interpretations to the available variations.<n>This allows us to obtain natural language descriptions and visual examples of the induced semantic shifts.<n>For example, our method reveals that ResNet50 leaks relevant semantic attributes to the null space, whereas DinoViT, a ViT pretrained with self-supervised DINO, is superior in maintaining class semantics across the invariant space.
• Score: 7.425558351422133
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: All classifiers, including state-of-the-art vision models, possess invariants, partially rooted in the geometry of their linear mappings. These invariants, which reside in the null-space of the classifier, induce equivalent sets of inputs that map to identical outputs. The semantic content of these invariants remains vague, as existing approaches struggle to provide human-interpretable information. To address this gap, we present Semantic Interpretation of the Null-space Geometry (SING), a method that constructs equivalent images, with respect to the network, and assigns semantic interpretations to the available variations. We use a mapping from network features to multi-modal vision language models. This allows us to obtain natural language descriptions and visual examples of the induced semantic shifts. SING can be applied to a single image, uncovering local invariants, or to sets of images, allowing a breadth of statistical analysis at the class and model levels. For example, our method reveals that ResNet50 leaks relevant semantic attributes to the null space, whereas DinoViT, a ViT pretrained with self-supervised DINO, is superior in maintaining class semantics across the invariant space.

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