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
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Deep Domain Generalization with Feature-norm Network.
TL;DR: In this article, an end-to-end feature-norm network (FNN) was proposed to tackle the problem of training with multiple source domains with the aim of generalizing to new domains at test time without an adaptation step.
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Extending Partial Domain Adaptation Algorithms to the Open-Set Setting
TL;DR: It is shown that the effectiveness of ANN methods utilized in the PDA setting is hindered by outlier target instances, and an adaptation for effective OSDA is proposed.
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Dual teacher-student based separation mechanism for open set domain adaptation
Shengsheng Wang,Bilin Wang +1 more
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Towards adaptive unknown authentication for universal domain adaptation by classifier paradox
TL;DR: In this paper , a composite classifier is jointly designed with two types of predictors, a multi-class predictor classifies samples to one of the multiple source classes, while a binary one-vs-all predictor further verifies the prediction by MC predictor.
Book ChapterDOI
Unknown-Oriented Learning for Open Set Domain Adaptation
Jie Liu,Xiao Lin Guo,Yixuan Yuan +2 more
TL;DR: Zhang et al. as discussed by the authors proposed a novel Unknown-Oriented Learning (UOL) framework for open set domain adaptation (OSDA), which is composed of three stages: true unknown excavation, false unknown suppression and known alignment.
References
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ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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