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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Sea fog detection based on unsupervised domain adaptation
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Hierarchical feature disentangling network for universal domain adaptation
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Domain adversarial neural network-based oil palm detection using high-resolution satellite images
TL;DR: A domain adaptation based approach for oil palm detection across two different high-resolution satellite images is proposed and improves accuracy by 25.39% in terms of F1-score in the target domain, and performs 9.04%-15.30% better than existing domain adaptation methods.
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Curriculum Feature Alignment Domain Adaptation for Epithelium-Stroma Classification in Histopathological Images
TL;DR: A Curriculum Feature Alignment Network (CFAN) is proposed to gradually align discriminative features across domains through selecting effective samples from the target domain and minimizing intra-class differences.
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An Open Set Domain Adaptation Algorithm via Exploring Transferability and Discriminability for Remote Sensing Image Scene Classification
TL;DR: Wang et al. as mentioned in this paper proposed an open set domain adaptation algorithm via exploring transferability and discriminability (OSDA-ETD), which aims at the high interdomain variations and high intraclass diversity of remote sensing images.
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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