Book ChapterDOI
Adapting visual category models to new domains
Kate Saenko,Brian Kulis,Mario Fritz,Trevor Darrell +3 more
- pp 213-226
TLDR
This paper introduces a method that adapts object models acquired in a particular visual domain to new imaging conditions by learning a transformation that minimizes the effect of domain-induced changes in the feature distribution.Abstract:
Domain adaptation is an important emerging topic in computer vision. In this paper, we present one of the first studies of domain shift in the context of object recognition. We introduce a method that adapts object models acquired in a particular visual domain to new imaging conditions by learning a transformation that minimizes the effect of domain-induced changes in the feature distribution. The transformation is learned in a supervised manner and can be applied to categories for which there are no labeled examples in the new domain. While we focus our evaluation on object recognition tasks, the transform-based adaptation technique we develop is general and could be applied to nonimage data. Another contribution is a new multi-domain object database, freely available for download. We experimentally demonstrate the ability of our method to improve recognition on categories with few or no target domain labels and moderate to large changes in the imaging conditions.read more
Citations
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Book ChapterDOI
Domain-adversarial training of neural networks
Yaroslav Ganin,Evgeniya Ustinova,Hana Ajakan,Pascal Germain,Hugo Larochelle,François Laviolette,Mario Marchand,Victor Lempitsky +7 more
TL;DR: In this article, a new representation learning approach for domain adaptation is proposed, in which data at training and test time come from similar but different distributions, and features that cannot discriminate between the training (source) and test (target) domains are used to promote the emergence of features that are discriminative for the main learning task on the source domain.
Proceedings ArticleDOI
Adversarial Discriminative Domain Adaptation
TL;DR: Adversarial Discriminative Domain Adaptation (ADDA) as mentioned in this paper combines discriminative modeling, untied weight sharing, and a generative adversarial network (GAN) loss.
Proceedings Article
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
TL;DR: DeCAF as discussed by the authors is an open-source implementation of these deep convolutional activation features, along with all associated network parameters, to enable vision researchers to conduct experimentation with deep representations across a range of visual concept learning paradigms.
Posted Content
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
TL;DR: DeCAF, an open-source implementation of deep convolutional activation features, along with all associated network parameters, are released to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms.
Posted Content
Learning Transferable Features with Deep Adaptation Networks
TL;DR: A new Deep Adaptation Network (DAN) architecture is proposed, which generalizes deep convolutional neural network to the domain adaptation scenario and can learn transferable features with statistical guarantees, and can scale linearly by unbiased estimate of kernel embedding.
References
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Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
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Proceedings ArticleDOI
Representing shape with a spatial pyramid kernel
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