Few-shot learning with improved local representations via bias rectify module
This work addresses performance limitations in few-shot learning for computer vision, though it appears incremental by building on existing metric learning approaches.
The paper tackles the problem of intra-class variations and limited spatial information in few-shot learning by proposing a Deep Bias Rectify Network with a bias rectify module and prototype augment mechanism, achieving state-of-the-art performance on popular benchmarks.
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.