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
Exploring Category Attention for Open Set Domain Adaptation
TL;DR: In this article, the authors propose a two-stage method to deal with the more challenging task of open set domain adaptation, where the target domain contains categories unseen to the source domain, and the first stage formulates the alignment of two domains as a semi-supervised clustering problem, and initially associates each target-domain sample with a source-domain category label.
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TL;DR: Zhang et al. as mentioned in this paper proposed an asymmetric co-teaching framework, which resists noisy labels by cooperating two models to select data with possibly clean labels for each other.
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References
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