  Recent approaches based on metric learning have achieved great progress in
few-shot learning. However, most of them are limited to image-level
representation manners, which fail to properly deal with the intra-class
variations and spatial knowledge and thus produce undesirable performance. In
this paper we propose a Deep Bias Rectify Network (DBRN) to fully exploit the
spatial information that exists in the structure of the feature
representations. We first employ a bias rectify module to alleviate the adverse
impact caused by the intra-class variations. bias rectify module is able to
focus on the features that are more discriminative for classification by given
different weights. To make full use of the training data, we design a prototype
augment mechanism that can make the prototypes generated from the support set
to be more representative. To validate the effectiveness of our method, we
conducted extensive experiments on various popular few-shot classification
benchmarks and our methods can outperform state-of-the-art methods.
