In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition. Specifically, we design a new model called Deep Reconstruction-Classification Network (DRCN), which jointly learns a shared encoding representation for two tasks: i) supervised classification of labeled source data, and ii) unsupervised reconstruction of unlabeled target data.In this way, the learnt representation not only preserves discriminability, but also encodes useful information from the target domain. Our new DRCN model can be optimized by using backpropagation similarly as the standard neural networks. We evaluate the performance of DRCN on a series of cross-domain object recognition tasks, where DRCN provides a considerable improvement (up to 8 in accuracy) over the prior state-of-the-art algorithms. Interestingly, we also observe that the reconstruction pipeline of DRCN transforms images from the source domain into images whose appearance resembles the target dataset. This suggests that DRCN's performance is due to constructing a single composite representation that encodes information about both the structure of target images and the classification of source images. Finally, we provide a formal analysis to justify the algorithm's objective in domain adaptation context.
Domain adaptation is a large field of research, with related work under several names such as class imbalance @cite_14 , covariate shift @cite_46 , and sample selection bias @cite_18 . In @cite_47 , it is considered as a special case of transfer learning. Earlier work on domain adaptation focused on text document analysis and NLP @cite_27 @cite_13 . In recent years, it has gained a lot of attention in the computer vision community, mainly for object recognition application, see @cite_33 and references therein. The domain adaptation problem is often referred to as in computer vision @cite_0 .
@cite_18 @cite_14 @cite_33 @cite_46 @cite_0 @cite_27 @cite_47 @cite_13
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Domain adaptation is an active, emerging research area that attemptsto address the changes in data distribution across training and testingdatasets. With the availability of a multitude of image acquisition sensors,variations due to illumination, and viewpoint among others, computervision applications present a very natural test bed for evaluatingdomain adaptation methods. In this monograph, we provide a comprehensiveoverview of domain adaptation solutions for visual recognitionproblems. By starting with the problem description and illustrations,we discuss three adaptation scenarios namely, i unsupervised adaptationwhere the "source domain" training data is partially labeledand the "target domain" test data is unlabeled, ii semi-supervisedadaptation where the target domain also has partial labels, and iiimulti-domain heterogeneous adaptation which studies the previous twosettings with the source and or target having more than one domain,and accounts for cases where the features used to represent the datain each domain are different. For all these topics we discuss existingadaptation techniques in the literature, which are motivated by theprinciples of max-margin discriminative learning, manifold learning,sparse coding, as well as low-rank representations. These techniqueshave shown improved performance on a variety of applications suchas object recognition, face recognition, activity analysis, concept classification,and person detection. We then conclude by analyzing thechallenges posed by the realm of "big visual data", in terms of thegeneralization ability of adaptation algorithms to unconstrained dataacquisition as well as issues related to their computational tractability,and draw parallels with the efforts from vision community on imagetransformation models, and invariant descriptors so as to facilitate improvedunderstanding of vision problems under uncertainty. 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. Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but described by another observation system. Among the many strategies proposed, finding domain-invariant representations has shown excellent properties, in particular since it allows to train a unique classifier effective in all domains. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labeled samples of the same class in the source domain to remain close during transport. This way, we exploit at the same time the labeled samples in the source and the distributions observed in both domains. Experiments on toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches. In addition, numerical experiments show that our approach leads to better performances on domain invariant deep learning features and can be easily adapted to the semi-supervised case where few labeled samples are available in the target domain. In many real-world applications, the domain of model learning (referred as source domain) is usually inconsistent with or even different from the domain of testing (referred as target domain), which makes the learnt model degenerate in target domain, i.e., the test domain. To alleviate the discrepancy between source and target domains, we propose a domain adaptation method, named as Bi-shifting Auto-Encoder network (BAE). The proposed BAE attempts to shift source domain samples to target domain, and also shift the target domain samples to source domain. The non-linear transformation of BAE ensures the feasibility of shifting between domains, and the distribution consistency between the shifted domain and the desirable domain is constrained by sparse reconstruction between them. As a result, the shifted source domain is supervised and follows similar distribution as target domain. Therefore, any supervised method can be applied on the shifted source domain to train a classifier for classification in target domain. The proposed method is evaluated on three domain adaptation scenarios of face recognition, i.e., domain adaptation across view angle, ethnicity, and imaging sensor, and the promising results demonstrate that our proposed BAE can shift samples between domains and thus effectively deal with the domain discrepancy.