Transfer Learning for Cross-Dataset Recognition: A Survey <s> Sequential Unlabelled Target Data <s> In real-world applications of visual recognition, many factors — such as pose, illumination, or image quality — can cause a significant mismatch between the source domain on which classifiers are trained and the target domain to which those classifiers are applied. As such, the classifiers often perform poorly on the target domain. Domain adaptation techniques aim to correct the mismatch. Existing approaches have concentrated on learning feature representations that are invariant across domains, and they often do not directly exploit low-dimensional structures that are intrinsic to many vision datasets. In this paper, we propose a new kernel-based method that takes advantage of such structures. Our geodesic flow kernel models domain shift by integrating an infinite number of subspaces that characterize changes in geometric and statistical properties from the source to the target domain. Our approach is computationally advantageous, automatically inferring important algorithmic parameters without requiring extensive cross-validation or labeled data from either domain. We also introduce a metric that reliably measures the adaptability between a pair of source and target domains. For a given target domain and several source domains, the metric can be used to automatically select the optimal source domain to adapt and avoid less desirable ones. Empirical studies on standard datasets demonstrate the advantages of our approach over competing methods. <s> BIB001 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Sequential Unlabelled Target Data <s> In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces described by eigenvectors. In this context, our method seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one. We show that the solution of the corresponding optimization problem can be obtained in a simple closed form, leading to an extremely fast algorithm. We use a theoretical result to tune the unique hyper parameter corresponding to the size of the subspaces. We run our method on various datasets and show that, despite its intrinsic simplicity, it outperforms state of the art DA methods. <s> BIB002 </s> Transfer Learning for Cross-Dataset Recognition: A Survey <s> Sequential Unlabelled Target Data <s> Almost all of the existing domain adaptation methods assume that all test data belong to a single stationary target distribution. However, in many real world applications, data arrive sequentially and the data distribution is continuously evolving. In this paper, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced. We assume that the available data for the source domain are labeled but the examples of the target domain can be unlabeled and arrive sequentially. Moreover, the distribution of the target domain can evolve continuously over time. We propose the Evolving Domain Adaptation (EDA) method that first finds a new feature space in which the source domain and the current target domain are approximately indistinguishable. Therefore, source and target domain data are similarly distributed in the new feature space and we use a semi-supervised classification method to utilize both the unlabeled data of the target domain and the labeled data of the source domain. Since test data arrives sequentially, we propose an incremental approach both for finding the new feature space and for semi-supervised classification. Experiments on several real datasets demonstrate the superiority of our proposed method in comparison to the other recent methods. <s> BIB003
Similar to problem in 3.5, the target data are sequential in this problem, however, no labelled target data is available, which is named unsupervised online domain adaptation and related to but different from concept drift. The concept drift  refers to changes in the conditional distribution of the output given the input, while the distribution of the input stays unchanged, while in online domain adaptation the changes between domains are caused by the changes of the input distribution. 3.6.1. Geometric Criterion. BIB002 ] to continuous evolving target domain. Both the subspaces and subspace metrics that align the two subspaces are updated after each new target sample comes. BIB003 tackle the continuously evolving target domain by using the idea of GFK BIB001 ] to construct linear transformation. The linear transformation is updated after a new batch of unlabelled target domain data come. Each batch of arrived target data are classified after the transformation and included to the source domain for recognizing the next batch of target data.