Bayesian few-shot classification has been a focal point in the field of few-shot learning. This paper seamlessly integrates mirror descent-based variational inference into Gaussian process-based few-shot classification, addressing the challenge of non-conjugate inference. By leveraging non-Euclidean geometry, mirror descent achieves accelerated convergence by providing the steepest descent direction along the corresponding manifold. It also exhibits the parameterization invariance property concerning the variational distribution. Experimental results demonstrate competitive classification accuracy, improved uncertainty quantification, and faster convergence compared to baseline models. Additionally, we investigate the impact of hyperparameters and components. Code is publicly available at https://github.com/keanson/MD-BSFC.

本研究论文通过将基于镜像下降的变分推断与基于高斯过程的少样本分类无缝集成，解决了非共轭推断所带来的挑战。借助非欧几何，镜像下降通过提供沿对应流形的最陡下降方向实现加速收敛。此方法还具有关于变分分布的参数化不变性。实验结果显示了与基准模型相比具有竞争力的分类准确性、改进的不确定性量化和更快的收敛速度。此外，我们还研究了超参数和组件的影响。代码公开可用于此 https URL。

贝叶斯少样本分类中的收敛加速