  Few-shot-learning (FSL) commonly requires a model to identify images
(queries) that belong to classes unseen during training, based on a few
labelled samples of the new classes (support set) as reference. So far, plenty
of algorithms involve training data augmentation to improve the generalization
capability of FSL models, but outlier query or support images during inference
can still pose great generalization challenges. In this work, to reduce the
bias caused by unconventional test samples, we generate additional test-class
samples through combining them with suitable train-class samples, whose
averaged representations then become more typical for few-shot classification.
We experimentally and theoretically demonstrate the effectiveness of our
method, via which we can achieve around 4\% test accuracy improvement for
trained FSL models. Importantly, our method is universally compatible with
different off-the-shelf FSL models, whose performance can be improved without
extra dataset nor further training of the models themselves.
