基于特征变换和度量网络的小样本学习算法OA北大核心CSTPCD

For few-shot classification,training samples for each class are highly limited.Consequently,samples from the same class tend to distribute sparsely while boundaries between different classes are indistinct in the feature space.Therefore,a novel few-shot learning algorithm based on feature transformation and metric networks(FTMN)is proposed for few-shot learning.The algorithm maps samples to the feature space through an embedding function and calculates the residual between the input features and their class center.A feature transformation function is then constructed to learn from the residual,enabling input features to move closer to their class center after trans-formation.The transformed features are used to update the class centers,increasing the distance between centers of different classes.Furthermore,the algorithm introduces a novel metric function that jointly expresses the metric distances of each point within the features.The metric function simultaneously optimizes both cosine similarity and Euclidean distance.The performance of the algorithm on commonly used datasets for few-shot classification valid-ates its effectiveness and generalization ability.