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    Hybrid Granularity Distribution Estimation (HGDE) improves few-shot learning by combining category and instance statistics. This novel approach enhances sample representativeness and boosts accuracy in few-shot tasks.

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    Area of Science:

    • Machine Learning
    • Computer Vision

    Background:

    • Few-shot learning (FSL) faces challenges due to data scarcity.
    • Category-level distribution estimation in FSL can lead to suboptimal performance because of dissimilarities between base and novel categories.

    Purpose of the Study:

    • To introduce Hybrid Granularity Distribution Estimation (HGDE) for more effective distribution estimation in FSL.
    • To enhance the characterization of novel categories by integrating both coarse-grained and fine-grained statistics.

    Main Methods:

    • HGDE integrates category-level statistics with instance-level statistics from nearest base samples.
    • Statistics are fused using linear interpolation to create a robust distribution for novel categories.
    • Refined estimation techniques, including weighted summation for mean and principal component retention for covariance, are employed.

    Main Results:

    • HGDE demonstrated effective distribution estimation capabilities across four FSL benchmarks (Mini-ImageNet, Tiered-ImageNet, CUB, CIFAR-FS).
    • Notable accuracy gains were observed, with over 1.8% improvement in 1-shot tasks on CUB.
    • The method effectively balances mean precision and variance diversity.

    Conclusions:

    • HGDE offers a versatile and effective solution for few-shot learning by improving distribution estimation.
    • The hybrid approach captures subtle features overlooked by category-level estimation alone.
    • HGDE enhances sample diversity and representativeness, leading to improved FSL performance.