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• W2772465720 abstract "While deep learning has led to significant advances in visual recognition over the past few years, such advances often require a lot of annotated data. Unsupervised domain adaptation has emerged as an alternative approach that does not require as much annotated data, prior evaluations of domain adaptation approaches have been limited to relatively similar datasets, e.g source and target domains are samples captured by different cameras. A new data suite is proposed that comprehensively evaluates cross-modality domain adaptation problems. This work pushes the limit of unsupervised domain adaptation through an in-depth evaluation of several state of the art methods on benchmark datasets and the new dataset suite. We also propose a new domain adaptation network called Deep MagNet that effectively transfers knowledge for cross-modality domain adaptation problems. Deep Magnet achieves state of the art performance on two benchmark datasets. More importantly, the proposed method shows consistent improvements in performance on the newly proposed dataset suite." @default.
• W2772465720 created "2017-12-22" @default.
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• W2772465720 date "2017-12-06" @default.
• W2772465720 modified "2023-10-09" @default.
• W2772465720 title "Stretching Domain Adaptation: How far is too far?" @default.
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• W2772465720 doi "https://doi.org/10.48550/arxiv.1712.02286" @default.
• W2772465720 hasPublicationYear "2017" @default.
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