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    Debugging complex deep-learning models like temporal multilabel classification (TMLC) is challenging. We introduce DETOXER, an interactive visual system to help debug TMLC models more effectively.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Deep learning models require iterative debugging for performance enhancement.
    • Temporal multilabel classification (TMLC) presents unique debugging challenges due to multi-class temporal data.
    • Video activity recognition is a key application area for TMLC models.

    Purpose of the Study:

    • To address the complexities in debugging TMLC models.
    • To introduce an interactive visual debugging system for TMLC applications.
    • To enhance the identification of diverse error types and scopes in video activity recognition.

    Main Methods:

    • Development of DETOXER, an interactive visual debugging system.
    • Focus on video activity recognition as a TMLC application.
    • Implementation of multiscope explanations for error analysis.

    Main Results:

    • DETOXER provides interactive visual support for debugging.
    • The system facilitates the identification of various error types.
    • Multiscope explanations aid in understanding error scopes within TMLC models.

    Conclusions:

    • DETOXER offers a novel approach to debugging complex TMLC models.
    • The system improves the efficiency and effectiveness of model refinement.
    • Visual and interactive debugging is crucial for advanced AI applications like video analysis.