基于特征变换和度量网络的小样本学习算法

Feature Transformation and Metric Networks for Few-shot Learning

在小样本分类任务中,每个类别可供训练的样本数量非常有限.因此在特征空间中同类样本分布稀疏,异类样本间边界模糊.提出一种新的基于特征变换和度量网络(Feature transformation and metric networks,FTMN)的小样本学习算法用于小样本分类任务.算法通过嵌入函数将样本映射到特征空间,并计算输入该样本与所属类别中心的特征残差.构造一个特征变换函数对该残差进行学习,使特征空间内的样本特征经过该函数后向同类样本中心靠拢.利用变换后的样本特征更新类别中心,使各类别中心间的距离增大.算法进一步构造了一种新的度量函数,对样本特征中每个局部特征点的度量距离进行联合表达,该函数能够同时对样本特征间的夹角和欧氏距离进行优化.算法在小样本分类任务常用数据集上的优秀表现证明了算法的有效性和泛化性.

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 transformation.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 validates its effectiveness and generalization ability.