用几何平均改进少样本学习损失函数，提升分类判别性
Geometric Mean Improves Loss For Few-Shot Learning
- 用几何平均聚合样本间关系，构建更具判别性的特征度量
- 在少样本图像分类任务中达到与主流损失相当的性能
- 方法简洁且理论分析充分，适合少样本学习研究者
少样本学习（FSL）是机器学习中的挑战性任务，要求模型仅用少量标注样本即可实现有效分类。现有FSL方法通常通过度量学习训练深度模型，在特征空间中构建可泛化的度量，使新类别仅凭少量样本即可形成有效分类器。本文提出一种基于几何平均的新颖FSL损失函数，将判别性度量嵌入深层特征。相比传统基于算术平均的softmax损失，该方法利用几何平均聚合样本对之间的关系，增强类别间的判别性度量。所提损失形式简洁，并通过理论分析揭示其在学习特征度量方面的优势。在多个少样本图像分类任务上的实验表明，该方法性能与现有损失相当，具备竞争力。
原文摘要 · Abstract (English)
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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