  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.
