基于自监督知识的无监督新集域适应学习

Unsupervised New-set Domain Adaptation with Self-supervised Knowledge

无监督域适应(unsupervised domain adaptation,UDA)旨在利用带大量标注数据的源域帮助无任何标注信息的目标域学习.在UDA中,通常假设源域和目标域间的数据分布不同,但共享相同的类标签空间.但在真实开放学习场景中,域间的标签空间很可能存在差异.在极端情形下,域间的类别不存在交集,即目标域中类别都为新未知类别.此时若直接迁移源域的类判别知识,可能会损害目标域性能,导致负迁移问题.为此,提出了基于自监督知识的无监督新集域适应(unsupervised new-set domain adaptation with self-supervised knowledge,SUNDA)方法,迁移源域的样本对比知识;同时,利用目标域的自监督知识指导知识迁移.首先,通过自监督学习源域和目标域初始特征,并固定部分网络参数用于保存目标域信息.再将源域的样本对比知识迁移至目标域,辅助目标域学习类判别特征.此外,利用基于图的自监督分类损失,解决域间无共享类别时目标域的分类问题.在手写体数字的无共享类别跨域迁移和人脸数据的无共享类别跨种族迁移任务上对SUNDA进行评估,实验结果表明,SUNDA的学习性能优于无监督域适应、无监督聚类以及新类别发现方法.

Unsupervised domain adaptation(UDA)adopts source domain with large amounts of labeled data to help the learning of the target domain without any label information.In UDA,the source and target domains usually have different data distribution,but share the same label space.But in real open scenarios,label spaces between domains can also be different.In extreme cases,there is no shared class between domains,i.e.,all classes in target domain are new classes.In this case,directly transferring the discriminant knowledge in source domain would harm the performance of target domain,lead to negative transfer.As a result,this study proposes an unsupervised new-set domain adaptation with self-supervised knowledge(SUNDA).Firstly,self-supervised learning is adopted to learn the initial features on source and target domains,with the first few layers frozen,in order to keep the target information.Then,the class contrastive knowledge from the source domain is transferred,to help learning discriminant features for target domain.Moreover,the graph-based self-supervised classification loss is adopted to handle the classification problem in target domain without common labels.Experiments are conducted over both digit and face recognition tasks without shared classes,and the empirical results show the competitive of SUNDA compared with UDA and unsupervised clustering methods,as well as new category discovery method.