Unified Deep Supervised Domain Adaptation and Generalization
Saeid Motiian,Marco Piccirilli,Donald A. Adjeroh,Gianfranco Doretto +3 more
- pp 5716-5726
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TLDR
This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models by reverting to point-wise surrogates of distribution distances and similarities by exploiting the Siamese architecture.Abstract:
This work provides a unified framework for addressing the problem of visual supervised domain adaptation and generalization with deep models. The main idea is to exploit the Siamese architecture to learn an embedding subspace that is discriminative, and where mapped visual domains are semantically aligned and yet maximally separated. The supervised setting becomes attractive especially when only few target data samples need to be labeled. In this scenario, alignment and separation of semantic probability distributions is difficult because of the lack of data. We found that by reverting to point-wise surrogates of distribution distances and similarities provides an effective solution. In addition, the approach has a high “speed” of adaptation, which requires an extremely low number of labeled target training samples, even one per category can be effective. The approach is extended to domain generalization. For both applications the experiments show very promising results.read more
Citations
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Deep Frequency Filtering for Domain Generalization
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TL;DR: Extensive experiments demonstrate the effectiveness of the proposed Deep Frequency Filtering and show that applying DFF on a plain baseline outperforms the state-of-the-art methods on different domain generalization tasks, including close-set classification and open-set retrieval.
Book ChapterDOI
Unsupervised Domain Adaptation with Joint Domain-Adversarial Reconstruction Networks
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