Bayesian Probability Model Driven Few Shot Deep Learning Classification Method

Abstract

Few shot classification has emerged as a critical challenge in machine learning, aiming to enable models to recognize novel categories from very limited labeled samples. Conventional deep learning approaches typically rely on large scale annotated datasets, which are often impractical to obtain in real world applications. This study proposes a Bayesian probability model driven deep learning framework for few shot classification, integrating principles of probabilistic inference with deep neural architectures. Specifically, the framework incorporates prior distributions over model parameters and leverages variational inference to approximate posterior distributions, thereby enabling principled uncertainty quantification during the classification process. The research evaluates the proposed framework on three public benchmark datasets: miniImageNet, CIFAR FS, and tieredImageNet. The first dataset examines generic object recognition, the second focuses on fine grained classification, and the third assesses domain generalization. A mixed methods approach combining quantitative accuracy metrics and qualitative analysis of uncertainty estimates is employed. The results demonstrate that Bayesian probability model driven methods achieve superior classification performance compared to deterministic counterparts, particularly in very low data scenarios. However, limitations remain in computational efficiency and scalability to high dimensional feature spaces. This study contributes to the understanding of probabilistic deep learning in resource constrained environments, offering insights for future developments in few shot learning systems.