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In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
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Convolution computations can be simplified by utilizing their inherent properties.
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TraNCE: Transformative Nonlinear Concept Explainer for CNNs.

Ugochukwu Ejike Akpudo, Yongsheng Gao, Jun Zhou

    IEEE Transactions on Neural Networks and Learning Systems
    |April 16, 2025
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    Summary
    This summary is machine-generated.

    This study introduces TraNCE, a novel nonlinear explainer for Convolutional Neural Networks (CNNs), improving concept discovery and visualization. It addresses limitations in existing methods by capturing intricate activation relationships for more faithful and consistent AI explanations.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Convolutional Neural Networks (CNNs) excel in computer vision but lack inherent explainability.
    • Existing concept-based explainability methods often assume linear relationships in image activations, failing to capture complex patterns.
    • Current evaluation metrics for global explanations focus solely on fidelity, neglecting other crucial aspects.

    Purpose of the Study:

    • To introduce TraNCE (Transformative Nonlinear Concept Explainer), a novel method for enhancing CNN explainability.
    • To address the limitations of linear reconstruction assumptions and fidelity-only evaluation in existing explainability techniques.
    • To provide deeper insights into what CNNs see by revealing both recognized and avoided concepts.

    Main Methods:

    • Developed an automatic concept discovery mechanism using Variational Autoencoders (VAEs) for enhanced identification of meaningful concepts.
    • Implemented a visualization module employing the Bessel function for smooth transitions in image pixels, mitigating concept duplication.
    • Introduced the 'faith score,' a new metric integrating coherence and fidelity for comprehensive evaluation of explainer faithfulness.

    Main Results:

    • Demonstrated that nonlinear reconstruction is essential for accurate decomposition of high-dimensional image activations, improving explainer efficiency.
    • Quantitatively showed that concept consistency, alongside accuracy, is vital for meaningfulness and user trust in AI models.
    • Validated TraNCE's ability to reveal both what CNNs see and avoid, offering a more nuanced understanding.

    Conclusions:

    • TraNCE offers a significant advancement in CNN explainability by capturing nonlinear relationships within activations.
    • The developed methods enhance concept discovery, visualization, and evaluation, leading to more trustworthy AI systems.
    • This work underscores the importance of nonlinear reconstruction and consistency metrics for robust AI explainability.