  Many recent few-shot learning methods concentrate on designing novel model
architectures. In this paper, we instead show that with a simple backbone
convolutional network we can even surpass state-of-the-art classification
accuracy. The essential part that contributes to this superior performance is
an adversarial feature learning strategy that improves the generalization
capability of our model. In this work, adversarial features are those features
that can cause the classifier uncertain about its prediction. In order to
generate adversarial features, we firstly locate adversarial regions based on
the derivative of the entropy with respect to an averaging mask. Then we use
the adversarial region attention to aggregate the feature maps to obtain the
adversarial features. In this way, we can explore and exploit the entire
spatial area of the feature maps to mine more diverse discriminative knowledge.
We perform extensive model evaluations and analyses on miniImageNet and
tieredImageNet datasets demonstrating the effectiveness of the proposed method.
