Cross-domain few-shot classification (CDFSC) is a challenging and tough task due to the significant distribution discrepancies across different domains. To address this challenge, many approaches aim to learn transferable representations. Multilayer perceptron (MLP) has shown its capability to learn transferable representations in various downstream tasks, such as unsupervised image classification and supervised concept generalization. However, its potential in the few-shot settings has yet to be comprehensively explored. In this study, we investigate the potential of MLP to assist in addressing the challenges of CDFSC. Specifically, we introduce three distinct frameworks incorporating MLP in accordance with three types of few-shot classification methods to verify the effectiveness of MLP. We reveal that MLP can significantly enhance discriminative capabilities and alleviate distribution shifts, which can be supported by our expensive experiments involving 10 baseline models and 12 benchmark datasets. Furthermore, our method even compares favorably against other state-of-the-art CDFSC algorithms.

翻译：跨域少样本分类（CDFSC）因不同领域间存在显著的分布差异而极具挑战性。为应对这一难题，许多方法侧重于学习可迁移表征。多层感知器（MLP）已在无监督图像分类和有监督概念泛化等多种下游任务中展现出学习可迁移表征的能力，但其在少样本场景下的潜力尚未被充分探索。本研究旨在探究MLP在解决CDFSC挑战方面的潜力。具体而言，我们根据三类少样本分类方法，引入三种包含MLP的不同框架以验证其有效性。实验表明，MLP能显著增强判别能力并缓解分布偏移——这一结论基于涉及10个基线模型和12个基准数据集的充分实验。此外，我们的方法甚至能与当前最先进的CDFSC算法相媲美。