Prototype-Augmented Contrastive Learning for Few-Shot Unsupervised Domain Adaptation.
KSEM (4)(2023)
摘要
Unsupervised domain adaptation aims to learn a classification model from the source domain with much-supervised information, which is applied to the utterly unsupervised target domain. However, collecting enough labeled source samples is difficult in some scenarios, decreasing the effectiveness of previous approaches substantially. Therefore, a more challenging and applicable problem called few-shot unsupervised domain adaptation is considered in this work, where a classifier trained with only a few source labels needs to show strong generalization on the target domain. The prototype-based self-supervised learning method has presented superior performance improvements in addressing this problem, while the quality of the prototype could be further improved. To mitigate this situation, a novel Prototype-Augmented Contrastive Learning is proposed. A new computation strategy is utilized to rectify the source prototypes, which are then used to improve the target prototypes. To better learn semantic information and align features, both in-domain prototype contrastive learning and cross-domain prototype contrastive learning are performed. Extensive experiments are conducted on three widely used benchmarks: Office, OfficeHome, and DomainNet, achieving accuracy improvement of over 3%, 1%, and 0.5%, respectively, demonstrating the effectiveness of the proposed method.
更多查看译文
关键词
adaptation,learning,domain,prototype-augmented,few-shot
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要