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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep learning publications often lack standardized visualizations for neural network architectures.
    • Current visualization methods are typically handcrafted, leading to inconsistencies, time inefficiencies, and errors.
    • Existing automatic tools are designed for debugging, not for generating publication-ready figures.

    Purpose of the Study:

    • To automate the generation of publication-quality visualizations for neural network architectures.
    • To establish a common visual grammar for convolutional neural networks (CNNs).
    • To provide a tool that reduces time and ambiguity in creating network visualizations.

    Main Methods:

    • Developed an approach to translate Keras-specified network architectures into embeddable visualizations.
    • Proposed a visual grammar for CNNs based on analyzing figures from major computer vision conferences (ICCV, CVPR) between 2013-2019.
    • Incorporated visual encoding, network layout, layer aggregation, and legend generation into the grammar.
    • Implemented the approach as an online system for community use.

    Main Results:

    • The system successfully automates the creation of network visualizations, reducing manual effort.
    • The proposed visual grammar ensures a unified and unambiguous design for CNN visualizations.
    • Evaluation through expert feedback and quantitative study confirmed the system's effectiveness.

    Conclusions:

    • Automating neural network visualization with a defined grammar streamlines the publication process.
    • The developed system offers a standardized and efficient solution for creating clear and consistent visualizations.
    • This work promotes better communication of deep learning architectures in scientific literature.