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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Data Visualization

    Background:

    • Deep neural networks (DNNs) excel in vision tasks but their internal workings, especially class separability, are poorly understood.
    • Analyzing activation channels and layer contributions is crucial for interpreting DNN behavior.

    Purpose of the Study:

    • Introduce ChannelExplorer, an interactive visual analytics tool for analyzing DNNs.
    • Focus on data-driven insights to explore class separability across model layers.
    • Support understanding of diverse model architectures like CNNs, GANs, ResNet, and Stable Diffusion.

    Main Methods:

    • ChannelExplorer provides a dataset-level overview, progressively drilling down to individual examples.
    • Utilizes three coordinated views: Scatterplot View for class confusion, Jaccard Similarity View for activation overlap, and Heatmap View for channel patterns.
    • Summarizes activations across model layers to reveal insights into class separability.

    Main Results:

    • Demonstrated ChannelExplorer's utility in generating ImageNet class hierarchy.
    • Successfully used the tool to find mislabeled images within datasets.
    • Enabled identification of specific activation channel contributions to model performance.
    • Facilitated locating latent states within Stable Diffusion models.

    Conclusions:

    • ChannelExplorer offers a novel approach to understanding DNNs by focusing on class separability through visual analytics.
    • The tool is versatile, supporting various deep learning architectures and use cases.
    • Expert user evaluation confirmed the tool's effectiveness in providing actionable insights into DNN behavior.