Geometric Mean Improves Loss For Few-Shot Learning

• URL: http://arxiv.org/abs/2501.14593v1
• Date: Fri, 24 Jan 2025 15:56:55 GMT
• Title: Geometric Mean Improves Loss For Few-Shot Learning
• Authors: Tong Wu, Takumi Kobayashi,
• Abstract summary: We propose a novel loss based on emphgeometric mean to embed discriminative metric into deep features. In the experiments on few-shot image classification tasks, the method produces competitive performance in comparison to the other losses.
• Score: 22.919680257199754
• License:
• Abstract: Few-shot learning (FSL) is a challenging task in machine learning, demanding a model to render discriminative classification by using only a few labeled samples. In the literature of FSL, deep models are trained in a manner of metric learning to provide metric in a feature space which is well generalizable to classify samples of novel classes; in the space, even a few amount of labeled training examples can construct an effective classifier. In this paper, we propose a novel FSL loss based on \emph{geometric mean} to embed discriminative metric into deep features. In contrast to the other losses such as utilizing arithmetic mean in softmax-based formulation, the proposed method leverages geometric mean to aggregate pair-wise relationships among samples for enhancing discriminative metric across class categories. The proposed loss is not only formulated in a simple form but also is thoroughly analyzed in theoretical ways to reveal its favorable characteristics which are favorable for learning feature metric in FSL. In the experiments on few-shot image classification tasks, the method produces competitive performance in comparison to the other losses.

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