ADAPTIVE PARAMETRIC PROTOTYPE LEARNING FOR CROSS-DOMAIN FEW-SHOT CLASSIFICATION

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 fewshot 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-movingaverage 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.

1. INTRODUCTION

Benefiting from the development of deep neural networks, significant advancement has been achieved on image classification with large amounts of annotated data. However, obtaining large amounts of annotated data is time-consuming and labour-intensive, while it is difficult to generalize trained models to new categories of data. As a solution, few-shot learning (FSL) has been proposed to classify instances from unseen classes using only a few labeled instances. FSL methods usually use a base dataset with labeled images to train a prediction model in the training phase. The model is then fine-tuned on the prediction task of novel categories with a few labeled instances (i.e. support set), and finally evaluated on the test data (i.e. query set) from the same novel categories in the testing phase. FSL has been widely studied in the in-domain settings where the training and test tasks are from the same domain (Finn et al., 2017; Snell et al., 2017; Lee et al., 2019) . However, when the training and test tasks are in different domains, it poses a much more challenging cross-domain few-shot learning problem than its in-domain counterpart due to the domain shift problem. Recently, several methods have made progress to address cross-domain few-shot learning, including the ones based on data augmentation, data generation (Wang & Deng, 2021; Yeh et al., 2020; Islam et al., 2021) and self-supervised learning (Phoo & Hariharan, 2020) techniques. However, such data generation and augmentation methods increase the computational cost and cannot scale well to scenarios with higher-shots (Wang & Deng, 2021) . Some other works either require large amounts of labeled data from multiple source domains (Hu et al., 2022) or the availability of substantial unlabeled data from the target domain during the source training phase (Phoo & Hariharan, 2020; Islam et al., 2021; Yao, 2021) . Such requirements are hard to meet and hence hamper their applicability in many domains. Although some existing prototypical-based few-shot methods have also been applied to address cross-domain few-shot learning due to their simplicity and computational efficiency (Snell et al., 2017; Satorras & Estrach, 2018) , these standard methods lack sufficient capacity in handing large cross-domain shifts and adapting to target domains. In this paper, we propose a novel Adaptive Parametric Prototype Learning (APPL) method under the meta-learning convention for cross-domain few-shot image classification. APPL introduces a parametric prototype calculator network (PCN) to learn class prototypes from concatenated feature vectors of the support instances by ensuring the inter-class discriminability and intra-class cohe-

