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

Marzi Heidari, Abdullah Alchihabi, Qing En, Yuhong Guo

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 methods.

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