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
Deep visual domain adaptation: A survey
Mei Wang,Weihong Deng +1 more
TL;DR: Deep domain adaptation has emerged as a new learning technique to address the lack of massive amounts of labeled data as discussed by the authors, which leverages deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning.
Proceedings ArticleDOI
Domain Adaptive Faster R-CNN for Object Detection in the Wild
TL;DR: Zhang et al. as discussed by the authors designed two domain adaptation components, on image level and instance level, to reduce the domain discrepancy in Faster R-CNN, which is based on $$-divergence theory and is implemented by learning a domain classifier in adversarial training manner.
Proceedings ArticleDOI
Strong-Weak Distribution Alignment for Adaptive Object Detection
TL;DR: This work proposes an approach for unsupervised adaptation of object detectors from label-rich to label-poor domains which can significantly reduce annotation costs associated with detection, and designs the strong domain alignment model to only look at local receptive fields of the feature map.
Journal ArticleDOI
Recent Advances in Open Set Recognition: A Survey
TL;DR: This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, evaluation criteria, and algorithm comparisons to highlight the limitations of existing approaches and point out some promising subsequent research directions.
Proceedings ArticleDOI
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification
TL;DR: Zhun et al. as discussed by the authors proposed an exemplar memory to store features of the target domain and accommodate the three invariance properties, i.e., exemplar-invariance, camera invariance, and neighborhood invariance.
References
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Proceedings ArticleDOI
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