In this paper, we introduce a new idea for unsupervised domain adaptation via a remold of Prototypical Networks, which learn an embedding space and perform classification via a remold of the distances to the prototype of each class. Specifically, we present Transferrable Prototypical Networks (TPN) for adaptation such that the prototypes for each class in source and target domains are close in the embedding space and the score distributions predicted by prototypes separately on source and target data are similar. Technically, TPN initially matches each target example to the nearest prototype in the source domain and assigns an example a "pseudo" label. The prototype of each class could then be computed on source-only, target-only and source-target data, respectively. The optimization of TPN is end-to-end trained by jointly minimizing the distance across the prototypes on three types of data and KL-divergence of score distributions output by each pair of the prototypes. Extensive experiments are conducted on the transfers across MNIST, USPS and SVHN datasets, and superior results are reported when comparing to state-of-the-art approaches. More remarkably, we obtain an accuracy of 80.4% of single model on VisDA 2017 dataset.

该研究介绍了一种新的无监督领域适应方法，通过重新设计Prototype Networks学习一个嵌入空间，并通过到每个类别原型的距离的重新设计进行分类。通过在源域和目标域中接近每个类别的原型，同时预测分别在源数据和目标数据上的原型的分数分布相似，实现Transferrable Prototypical Networks（TPN）自适应。在MNIST、USPS和SVHN数据集的转换中进行了大量实验证明，当与最先进的方法进行比较时，我们报告了更好的结果。更为重要的是，我们在2017年VisDA数据集上获得了80.4％的单模型准确率。

无监督领域自适应的可迁移原型网络