DEEP CLUSTERING FOR DOMAIN ADAPTATION

被引：0

作者：

Gao, Boyan ^{[1
]}

Yang, Yongxin ^{[1
]}

Gouk, Henry ^{[1
]}

Hospedales, Timothy M. ^{[1
,2
]}

机构：

[1] Univ Edinburgh, Sch Informat, Edinburgh, Midlothian, Scotland

[2] Samsung AI Ctr, Cambridge, England

来源：

2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING | 2020年

基金：

英国工程与自然科学研究理事会;

关键词：

Domain Adaptation; Deep Clustering; Unsupervised Learning; Semi-Supervised Learning;

D O I：

10.1109/icassp40776.2020.9053622

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

We address the heterogeneous domain adaptation task: adapting a classifier trained on data from one domain to operate on another domain that also has a different label space. We consider two settings that both exhibit label scarcity of some form-one where only unlabelled data is available, and another where a small volume of labelled data is available in addition to the unlabelled data. Our method is based on two specialisations of a recently proposed approach for deep clustering. It is shown that our approach noticeably outperforms other methods based on deep clustering in both the fully unsupervised and the semi-supervised settings.

引用

页码：4247 / 4251

页数：5

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