Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2019

Abstract

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 KLdivergence 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-theart approaches. More remarkably, we obtain an accuracy of 80.4% of single model on VisDA 2017 dataset.

Keywords

Categorization, Recognition: Detection, Retrieval

Discipline

Computer Sciences | OS and Networks

Research Areas

Intelligent Systems and Optimization

Publication

Proceedings of the 32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, California, June 16-21

First Page

2234

Last Page

2242

ISBN

9781728132938

Identifier

10.1109/CVPR.2019.00234

Publisher

IEEE Computer Society

City or Country

Long Beach

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