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Trainable label regions in classification-oriented diffractive neural networks.

Weifeng Ren, He Ren, Di Wang

    Optics Express
    |July 2, 2026
    PubMed
    Summary
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    This study introduces a label-trainable diffractive neural network (Lt-DNN) that optimizes label region configurations. This approach enhances classification accuracy and robustness in optical computing without added complexity.

    Area of Science:

    • Optics and photonics
    • Machine learning
    • Computer vision

    Background:

    • Diffractive neural networks (DNNs) are powerful optical computing tools.
    • The spatial configuration of label regions in DNNs is crucial for classification tasks but underexplored.
    • Fixed label regions limit the performance and adaptability of DNNs.

    Purpose of the Study:

    • To propose and evaluate a novel label-trainable diffractive neural network (Lt-DNN) framework.
    • To investigate the impact of learnable label region configurations on DNN performance.
    • To enhance classification accuracy, SNR, and robustness in optical neural computing.

    Main Methods:

    • Developed a label-trainable diffractive neural network (Lt-DNN) where label region positions and sizes are learnable parameters.

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  • Formulated training as a joint optimization problem using a latent-variable-based method for stochastic exploration.
  • Implemented a particle swarm optimization (PSO)-based decoupled framework to separate label optimization from network training.

    • Main Results:

    • Achieved improved classification accuracy, signal-to-noise ratio (SNR), and robustness compared to fixed-label configurations.
    • Demonstrated consistent performance across various datasets without additional optical components or increased inference complexity.
    • Showcased that label region configuration is a critical, underutilized degree of freedom in DNNs.

    Conclusions:

    • The proposed Lt-DNN framework effectively optimizes label regions, leading to significant performance gains in diffractive neural networks.
    • Label region configuration acts as a crucial decision layer, offering a new avenue for improving optical neural computing.
    • This approach enhances DNN capabilities without increasing hardware complexity or inference cost.