  Cross-domain few-shot classification task (CD-FSC) combines few-shot
classification with the requirement to generalize across domains represented by
datasets. This setup faces challenges originating from the limited labeled data
in each class and, additionally, from the domain shift between training and
test sets. In this paper, we introduce a novel training approach for existing
FSC models. It leverages on the explanation scores, obtained from existing
explanation methods when applied to the predictions of FSC models, computed for
intermediate feature maps of the models. Firstly, we tailor the layer-wise
relevance propagation (LRP) method to explain the predictions of FSC models.
Secondly, we develop a model-agnostic explanation-guided training strategy that
dynamically finds and emphasizes the features which are important for the
predictions. Our contribution does not target a novel explanation method but
lies in a novel application of explanations for the training phase. We show
that explanation-guided training effectively improves the model generalization.
We observe improved accuracy for three different FSC models: RelationNet, cross
attention network, and a graph neural network-based formulation, on five
few-shot learning datasets: miniImagenet, CUB, Cars, Places, and Plantae. The
source code is available at https://github.com/SunJiamei/few-shot-lrp-guided
