Proceedings ArticleDOI
Open Set Domain Adaptation
Pau Panareda Busto,Juergen Gall +1 more
- pp 754-763
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TLDR
This work learns a mapping from the source to the target domain by jointly solving an assignment problem that labels those target instances that potentially belong to the categories of interest present in the source dataset.Abstract:
When the training and the test data belong to different domains, the accuracy of an object classifier is significantly reduced. Therefore, several algorithms have been proposed in the last years to diminish the so called domain shift between datasets. However, all available evaluation protocols for domain adaptation describe a closed set recognition task, where both domains, namely source and target, contain exactly the same object classes. In this work, we also explore the field of domain adaptation in open sets, which is a more realistic scenario where only a few categories of interest are shared between source and target data. Therefore, we propose a method that fits in both closed and open set scenarios. The approach learns a mapping from the source to the target domain by jointly solving an assignment problem that labels those target instances that potentially belong to the categories of interest present in the source dataset. A thorough evaluation shows that our approach outperforms the state-of-the-art.read more
Citations
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Journal ArticleDOI
Open-set learning under covariate shift
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Transferable Discriminative Learning for Medical Open-Set Domain Adaptation: Application to Pneumonia Classification
TL;DR: Wang et al. as mentioned in this paper proposed transferable discriminative learning that remarkably achieves robust pneumonia classification with distribution shift and open class emerging, which can be used as a clinical tool for medical open-set domain adaptation.
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NI-UDA: Graph Adversarial Domain Adaptation from Non-shared-and-Imbalanced Big Data to Small Imbalanced Applications.
TL;DR: Zhang et al. as mentioned in this paper proposed a new general graph adversarial domain adaptation (GADA) based on semantic knowledge reasoning of class structure for solving the problem of unsupervised domain adaptation from the big data with non-shared and imbalanced classes to specified small and imbalance applications.
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LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation
TL;DR: In this article, a human-in-the-loop approach is proposed to adaptively give scarce labels to points that a UDA model is uncertain about, which reduces the effort from 2.2% segment label to 40 points label while minimizing performance degradation.
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Probabilistic Filtered Soft Labels for Domain Adaptation.
TL;DR: A well-designed Graph-based Label Propagation method is taken advantage of, and both of the class-wise MMD and class scatter matrice are modeled in this way to obtain more accurate filtered soft labels.
References
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