Separate to Adapt: Open Set Domain Adaptation via Progressive Separation

By Hong Liu, Zhangjie Cao, Mingsheng Long, Jianmin Wang, Qiang Yang. Computer Vision and Pattern Recognition, 2019

337 citations recorded by the source corpus.

Domain adaptation has become a resounding success in leveraging labeled data from a source domain to learn an accurate classifier for an unlabeled target domain. When deployed in the wild, the target domain usually contains unknown classes that are not observed in the source domain. Such setting is termed Open Set Domain Adaptation (OSDA). While several methods have been proposed to address OSDA, none of them takes into account the openness of the target domain, which is measured by the proportion of unknown classes in all target classes. Openness is a critical point in open set domain adaptation and exerts a significant impact on performance. In addition, current work aligns the entire target domain with the source domain without excluding unknown samples, which may give rise to negative transfer due to the mismatch between unknown and known classes. To this end, this paper presents Separate to Adapt (STA), an end-to-end approach to open set domain adaptation. The approach adopts a coarse-to-fine weighting mechanism to progressively separate the samples of unknown and known classes, and simultaneously weigh their importance on feature distribution alignment. Our approach allows openness-robust open set domain adaptation, which can be adaptive to a variety of openness in the target domain. We evaluate STA on several benchmark datasets of various openness levels. Results verify that STA significantly outperforms previous methods.

Subfields: Artificial Intelligence, Cancer Research

View Paper: https://www.semanticscholar.org/paper/e7243f56643f90c84d48b59ff2f2bf1ca8015bb7

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Citations in the corpus

Class-Incremental Domain Adaptation (2020, methodology): “Certain methods [7] achieve this even without accessing the source training data (hereon, we call such methods as source-free).”
Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study (2019, methodology): “Following previous work in [144], [150], [176], [177], five evaluation metrics are employed: 1) Accuracy of shared classes: ALL∗ = MS NS 2) Accuracy of unknown classes: UNK = MU NU 3) Averaged accuracy of all classes: OS = 1 C+1 ∑C c=0Accc 4) Accuracy of all test samples: ALL = MS+MU NS+NU 5) Harmon”
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation (2020, methodology): “For speciﬁc partial-set and open-set DA tasks, we compare with IWAN (Zhang et al., 2018a), SAN (Cao et al., 2018), ETN (Cao et al., 2019), SAFN, and ATI-λ (Panareda Busto & Gall, 2017), OSBP (Saito et al., 2018c), OpenMax (Bendale & Boult, 2016), STA (Liu et al., 2019), respectively.”
Domain Consensus Clustering for Universal Domain Adaptation (2021, background): “Recent works [38, 15] follow this paradigm to draw the knowledge from the domain discriminator to identify common samples that share the semantic classes across domains.”