SGBMN: Symplectic Group Bayesian Manifold Network for Few-shot Classification

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Particularly, employing meta-learning for few-shot classification has achieved remarkable advances. However, the uncertainty problem triggered by the noisy data or modeling assumptions hinders the performance of existing approaches to be further improved. To tackle the above issue, this paper proposes a novel meta-learning approach called Symplectic Group Bayesian Manifold Network (SGBMN) for a more accurate classification prediction. Specifically, we adopt symplectic group bayesian matrix to represent each input data point, such that the uncertainty problem caused by complex data could be alleviated. Then, a manifold space is constructed by combing the above symplectic group bayesian matrices, in which the optimization can be performed by natural gradient descent. We conduct extensive experiments on two real-world image datasets and the results demonstrate that our proposed method outperforms several baseline approaches.