Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification

• URL: http://arxiv.org/abs/2309.01342v1
• Date: Mon, 4 Sep 2023 03:58:50 GMT
• Title: Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
• Authors: Marzi Heidari, Abdullah Alchihabi, Qing En, Yuhong Guo
• Abstract summary: We develop a novel Adaptive Parametric Prototype Learning (APPL) method under the meta-learning convention for cross-domain few-shot classification. APPL yields superior performance than many state-of-the-art cross-domain few-shot learning methods.
• Score: 23.82751179819225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cross-domain few-shot classification induces a much more challenging problem than its in-domain counterpart due to the existence of domain shifts between the training and test tasks. In this paper, we develop a novel Adaptive Parametric Prototype Learning (APPL) method under the meta-learning convention for cross-domain few-shot classification. Different from existing prototypical few-shot methods that use the averages of support instances to calculate the class prototypes, we propose to learn class prototypes from the concatenated features of the support set in a parametric fashion and meta-learn the model by enforcing prototype-based regularization on the query set. In addition, we fine-tune the model in the target domain in a transductive manner using a weighted-moving-average self-training approach on the query instances. We conduct experiments on multiple cross-domain few-shot benchmark datasets. The empirical results demonstrate that APPL yields superior performance than many state-of-the-art cross-domain few-shot learning methods.

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