多标签小样本实例级注意力原型网络分类方法

多标签分类中，一个样本可能属于多个类别，且在小样本场景下模型性能更容易受到样本中复杂语义特征的影响。然而，目前常用的原型网络方法仅使用每类支持集样本的均值作为标签原型，导致原型中存在其他类别特征带来的噪声，弱化了原型间的差异性，影响预测效果。本文提出一种利用实例级注意力的多标签小样本原型网络分类方法，通过提高支持集中与当前标签关联度高的样本的权重，减少其他标签特征的干扰，增大标签原型之间的区分度，进而提高预测的精确率.实验表明，方法通过引入实例级注意力强化了多标签原型网络的学习能力，分类效果明显提升.

Prototypical Network with Instance-Level Attention in Multi-Label Few-Shot Learning

In multi-label classification, an instance may have multiple labels, and in few-shot scenario, the performance of model is more vulnerable to the complex semantic features in the instance. However, current prototype network only takes the mean value of instances in support set as label prototype. Therefore, there is noise caused by features of other labels in the calculated prototype, weakening the differences among prototypes, and affecting the prediction effect. To solve the above problem, a classification method was proposed for prototype network with instance-level attention in multi-label few-shot learning. This method was designed to reduce the interference resulted from features of other labels by increasing the weight of instances with high correlation between the support set and label, to improve the discrimination among prototypes, and further to improve the accuracy of prediction. The experimental results show that the proposed method can strengthen the learning ability of multi-label prototype network, and the classification effect is significantly improved.