  State of the art (SOTA) few-shot learning (FSL) methods suffer significant
performance drop in the presence of domain differences between source and
target datasets. The strong discrimination ability on the source dataset does
not necessarily translate to high classification accuracy on the target
dataset. In this work, we address this cross-domain few-shot learning (CDFSL)
problem by boosting the generalization capability of the model. Specifically,
we teach the model to capture broader variations of the feature distributions
with a novel noise-enhanced supervised autoencoder (NSAE). NSAE trains the
model by jointly reconstructing inputs and predicting the labels of inputs as
well as their reconstructed pairs. Theoretical analysis based on intra-class
correlation (ICC) shows that the feature embeddings learned from NSAE have
stronger discrimination and generalization abilities in the target domain. We
also take advantage of NSAE structure and propose a two-step fine-tuning
procedure that achieves better adaption and improves classification performance
in the target domain. Extensive experiments and ablation studies are conducted
to demonstrate the effectiveness of the proposed method. Experimental results
show that our proposed method consistently outperforms SOTA methods under
various conditions.
