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Updated: Dec 25, 2025

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    This survey reviews open set recognition (OSR) techniques for classifying known and unknown data. It covers methods, datasets, and future research directions for more robust AI systems.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Real-world AI tasks often lack complete data, making it hard to train classifiers for all possible classes.
    • Open set recognition (OSR) addresses this by requiring classifiers to handle both known and unknown classes during testing.

    Purpose of the Study:

    • To provide a comprehensive survey of current open set recognition (OSR) techniques.
    • To analyze OSR's relationship with related AI tasks and highlight research gaps.

    Main Methods:

    • Reviewing existing OSR literature, definitions, and models.
    • Comparing datasets, evaluation criteria, and algorithms.
    • Analyzing connections to zero-shot, few-shot learning, and classification with reject option.

    Main Results:

    • A structured overview of OSR methodologies and their comparative performance.
    • Identification of limitations in current OSR approaches.
    • Exploration of open world recognition as an extension of OSR.

    Conclusions:

    • The survey consolidates knowledge on OSR, offering a foundation for future research.
    • Identified limitations and future directions aim to advance the robustness of AI classifiers in real-world scenarios.