Exploring uncertainty in pseudo-label guided unsupervised domain adaptation
作者：
Highlights：
• We introduce the problem of addressing the uncertainties of target pseudo labels, which is important yet under-studied in the domain adaptation area.
• Specifically, we propose a novel approach that progressively includes more target samples into training and incorporates previously estimated class confidence scores to characterize both the within- and cross- domain relations. Especially, we provide a more accurate conditional distribution discrepancy than those of previous studies.
• To fully exploit the discriminative cross-domain structures, we compensate joint distribution adaptation by designing a new local triplet-wise instance-to-center margin for better separability.
• Experimental results demonstrate the superiority of our method over recent state-of-theart approaches. Particularly, on the challenging Office-Caltech dataset with VGG features show that our method advances the best reported average accuracies from 83.4% to 88.2% and 81.7% to 87.2%, respectively.
摘要
•We introduce the problem of addressing the uncertainties of target pseudo labels, which is important yet under-studied in the domain adaptation area.•Specifically, we propose a novel approach that progressively includes more target samples into training and incorporates previously estimated class confidence scores to characterize both the within- and cross- domain relations. Especially, we provide a more accurate conditional distribution discrepancy than those of previous studies.•To fully exploit the discriminative cross-domain structures, we compensate joint distribution adaptation by designing a new local triplet-wise instance-to-center margin for better separability.•Experimental results demonstrate the superiority of our method over recent state-of-theart approaches. Particularly, on the challenging Office-Caltech dataset with VGG features show that our method advances the best reported average accuracies from 83.4% to 88.2% and 81.7% to 87.2%, respectively.
论文关键词：Unsupervised domain adaptation,Pseudo labeling,Feature transformation,Progressive learning,Transfer learning
论文评审过程：Received 20 December 2018, Revised 25 June 2019, Accepted 31 July 2019, Available online 1 August 2019, Version of Record 9 August 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.106996
