Improved few-shot classification algorithm based on feature distribution calibration
The few-shot classification algorithm based on feature distribution calibration can not accurately reveal the feature distributions of novel classes.To address the issue and solve the N way-K shot few-shot image classification task,an improved algorithm combining latent space transform and density-based spatial clustering is proposed in this paper.Firstly,the deep features of base and novel images are extracted by breadth residual neural network.Then,the latent space transformation method is introduced to con-strain the feature distributions of novel classes so that it is closer to the normal distribution.Mean-while,the density-based spatial clustering method is employed to select a suitable base class for each novel class.Thus the statistical information of the base class is transferred to the novel class,and the sample can be effectively expanded by multivariate normal distribution matrix.Finally,a classifier based on ensemble learning is constructed to accomplish the classification task.The experimental results on two baseline datasets show that the proposed method can further improve the classification accuracy compared with the traditional feature distribution calibration model.
few-shot learningimage classificationfeature distribution calibrationlatent space transformdensity-based spatial clustering
NETL
NSTL
万方数据
