Task-adaptive Few-shot Learning on Sphere Manifold

Abstract

Few-shot learning aims to learn generalized knowledge from limited data, which is a challenging study. Metric-based few-shot learning is the most widely studied direction, and most metric-based methods focus on how to learn a more robust feature embedding space or distance metric for classification. Few-shot learning is trained with a large number of tasks. Most existing methods do not realize that the role of the same sample in different learning tasks may be different, which will affect the effectiveness of classification. This paper proposes a task-adaptive feature adjustment method to obtain new features with task context information and more discrimination to solve this problem. We also introduce the Sphere manifold into few-shot learning, use the manifold metric instead of the Euclidean metric, and deduce the gradient optimization on the manifold. We propose a novel few-shot learning method with an excellent performance by combining these two innovations. We conduct several experiments on the few-shot learning benchmark datasets to demonstrate the advancement of our methods. The experimental results have significantly improved over the baseline and are highly competitive with the mainstream and effective methods.