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Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions
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Characterizing Universal Object Representations Across Vision Models.

Florian P Mahner, Johannes Roth, Ka Chun Lam

    Arxiv
    |July 2, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Deep neural networks (DNNs) learn similar visual representations, but the underlying properties are unclear. This study reveals universal dimensions in DNNs, driven by semantic content and relevant to biological vision.

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    Area of Science:

    • Computer Vision
    • Computational Neuroscience
    • Artificial Intelligence

    Background:

    • Deep neural networks (DNNs) trained on diverse datasets often develop similar visual representations.
    • However, the specific visual properties and underlying factors driving this convergence remain largely unknown.

    Purpose of the Study:

    • To investigate which visual properties DNNs converge on.
    • To identify universal versus model-specific dimensions in DNN representations.
    • To explore factors influencing the emergence of universal dimensions and their relation to biological vision.

    Main Methods:

    • Decomposed the object similarity structure of 162 diverse vision models.
    • Analyzed the reappearance frequency of dimensions across models to identify universal dimensions.
    • Correlated universal dimensions with conceptual image properties, macaque IT activity, and human similarity judgments.

    Main Results:

    • Identified a small set of universal, interpretable dimensions in DNNs, driven by conceptual image properties.
    • Found that model architecture, objective, data, size, or performance did not explain universal dimensions.
    • Models with more universal dimensions better predicted biological vision data (macaque IT activity and human judgments).

    Conclusions:

    • Universality in DNN representations is driven by semantic content, not just training specifics.
    • Universal dimensions reflect representations aligned with biological vision.
    • Findings advance understanding of emergent representations in DNNs and their biological relevance.