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State of the art (SOTA) few-shot learning (FSL) methods suffer significantperformance drop in the presence of domain differences between source andtarget datasets. The strong discrimination ability on the source dataset doesnot necessarily translate to high classification accuracy on the targetdataset. 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 distributionswith a novel noise-enhanced supervised autoencoder (NSAE). NSAE trains themodel by jointly reconstructing inputs and predicting the labels of inputs aswell as their reconstructed pairs. Theoretical analysis based on intra-classcorrelation (ICC) shows that the feature embeddings learned from NSAE havestronger discrimination and generalization abilities in the target domain. Wealso take advantage of NSAE structure and propose a two-step fine-tuningprocedure that achieves better adaption and improves classification performancein the target domain. Extensive experiments and ablation studies are conductedto demonstrate the effectiveness of the proposed method. Experimental resultsshow that our proposed method consistently outperforms SOTA methods undervarious conditions.

Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder