Shuffle Augmentation of Features from Unlabeled Data for Unsupervised Domain Adaptation

Autor:	Xu, Changwei, Yang, Jianfei, Tang, Haoran, Zou, Han, Lu, Cheng, Zhang, Tianshuo
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning I.5.1 I.4.10
Druh dokumentu:	Working Paper
Popis:	Unsupervised Domain Adaptation (UDA), a branch of transfer learning where labels for target samples are unavailable, has been widely researched and developed in recent years with the help of adversarially trained models. Although existing UDA algorithms are able to guide neural networks to extract transferable and discriminative features, classifiers are merely trained under the supervision of labeled source data. Given the inevitable discrepancy between source and target domains, the classifiers can hardly be aware of the target classification boundaries. In this paper, Shuffle Augmentation of Features (SAF), a novel UDA framework, is proposed to address the problem by providing the classifier with supervisory signals from target feature representations. SAF learns from the target samples, adaptively distills class-aware target features, and implicitly guides the classifier to find comprehensive class borders. Demonstrated by extensive experiments, the SAF module can be integrated into any existing adversarial UDA models to achieve performance improvements. Comment: 17 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2201.11963 Zobrazit plný text záznamu View this record from Arxiv