  Few-shot learning (FSL) approaches are usually based on an assumption that
the pre-trained knowledge can be obtained from base (seen) categories and can
be well transferred to novel (unseen) categories. However, there is no
guarantee, especially for the latter part. This issue leads to the unknown
nature of the inference process in most FSL methods, which hampers its
application in some risk-sensitive areas. In this paper, we reveal a new way to
perform FSL for image classification, using visual representations from the
backbone model and weights generated by a newly-emerged explainable classifier.
The weighted representations only include a minimum number of distinguishable
features and the visualized weights can serve as an informative hint for the
FSL process. Finally, a discriminator will compare the representations of each
pair of the images in the support set and the query set. Pairs with the highest
scores will decide the classification results. Experimental results prove that
the proposed method can achieve both good accuracy and satisfactory
explainability on three mainstream datasets.
