Transfer metric learning for unsupervised domain adaptation

IET Image ProcessingVolume 13, Issue 5 p. 804-810 Research ArticleFree Access Transfer metric learning for unsupervised domain adaptation Junchu Huang, Junchu Huang School of Electronic and Information Engineering, South China University of Technology, GuangZhou, People's Republic of ChinaSearch for more papers by this authorZhiheng Zhou, Corresponding Author Zhiheng Zhou zhouzh@scut.edu.cn School of Electronic and Information Engineering, South China University of Technology, GuangZhou, People's Republic of ChinaSearch for more papers by this author Junchu Huang, Junchu Huang School of Electronic and Information Engineering, South China University of Technology, GuangZhou, People's Republic of ChinaSearch for more papers by this authorZhiheng Zhou, Corresponding Author Zhiheng Zhou zhouzh@scut.edu.cn School of Electronic and Information Engineering, South China University of Technology, GuangZhou, People's Republic of ChinaSearch for more papers by this author First published: 20 March 2019 https://doi.org/10.1049/iet-ipr.2018.5871Citations: 5AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Domain adaptation is still a challenging task due to the fact that the distribution discrepancy between source domain and target domain weakens the transfer ability. Intuitively, it is crucial to discover a more discriminative feature representation across domains. However, previous methods do not take the target discriminative information into account since (most) target data are unlabelled. Here, the authors propose a transfer metric learning method which decreases intra-class distance and increases inter-class distance simultaneously even in the case of target data are unlabelled. The shared features are more discriminative, hence the model could be more robust for target data. Specially, the global optimal solution can be obtained by solving a generalised eigen-decomposition problem. Extensive experiments on image datasets demonstrate that compared to several state-of-the-art methods, authors’ method achieves significant improvement of 9.0% in average classification accuracy. 1 Introduction A major assumption of traditional machine-learning algorithms is that the training and testing data follow the same distribution, which is the guarantee of generalisation ability. Failing to meet such assumption may cause significant performance degradation. To overcome this issue, transfer learning has been proposed for the purpose of leveraging knowledge from related well-labelled data (refer to as source domain) to boost the classification task of new data (refer to as target domain). As a significant branch of transfer learning, domain adaptation has been exploited in many fields, including image classification [1-5], video event detection [6], and activity recognition [7]. Domain adaptation can generalise a learning model across source domain and target domain that follow different distributions. There is no doubt that the distribution discrepancy between the source domain and the target domain weakens the transfer ability. As a result, a stream of excellent works [8-15] try to improve the classification performance of target domain via minimising domain discrepancy. There are several methods representing these ideas. Transfer Component Analysis (TCA) method [13] looks for domain feature invariants by reducing the cross-domain marginal distribution discrepancy in the reproducing kernel Hilbert space while Joint Distribution Adaptation (JDA) [10] method also considers the class conditional distribution adaptation in addition. Geodesic Flow Kernel (GFK) [14] method regards two subspaces as two points on Grassmann manifold, and bridges source and target subspaces via seeking smooth geodesic path. Transfer joint matching (TJM) method [12] reduces the domain difference by jointly matching the features and re-weighting the instances across domains. Joint Geometrical and Statistical Alignment (JGSA) [15] considers to preserve the source discriminative information and align distribution by two coupled projections. However, previous methods do not take the target discriminative information into account since (most) target data is unlabelled. To get a more robust transfer learning model, it is important to increase large enough margin for different classes (just as Fig. 1 demonstrates). As shown in B of Fig. 1, different classes are separated by a large enough margin. In classified hyperplane, there are still many indistinguishable samples because existing methods can only reduce, but not remove the cross-domain discrepancy. If the interval between different classes is not far enough (although the difference between the training and testing data has been reduced as shown in C of Fig. 1), the classification model cannot be robust. Fig. 1Open in figure viewerPowerPoint Motivating examples for the unsupervised metric transfer learning (UTML) formulation. Different classes use different markers. The black line represents the classification hyperplane trained by the labelled source data (black) and is used to classify unlabelled target data (orange) (a) Original data. (b) Only maximising the inter-class distance. (c) Only minimising the intra-class distance. (d) Not only maximising the inter-class distance but also minimising the intra-class distance If only source data are used to increase the inter-class distance and reduce intra-class distance, generalisation on target data will fail due to the distribution discrepancy of source data and target data. The key issue of this paper is to leverage the discriminative information from the target domain even if the target labels are not available. To realise this idea, we utilise the mean of each class pseudo-label (predicted by the classifier trained on source domain) in the target data because it is not sensitive to noise caused by the misclassification [16]. As a result, this paper proposes unsupervised metric transfer learning method (UMTL) to learn domain invariant features with more discriminative information via the Maximum Mean Discrepancy [17, 18]. The main contributions of this paper are summarised as follows. Our method could effectively integrate maximising inter-class distance and minimising intra-class distance into one framework, and obtain the global optimal solution by solving a generalised eigen-decomposition problem. As a result, standard machine-learning approaches can be easily applied on the newly learned features from the labelled source data for use in the unlabelled target data. To leverage the discriminative information of target domain, we consider the pseudo-labels of target data and thus we can alternatingly improve the labelling quality until convergence. Furthermore, we analyses time complexity of our method. Extensive experiments on image datasets demonstrate that compared to several state-of-the-art methods, our method achieves significant improvement of 9.0% in average classification accuracy. The rest of the paper is organised as follows. Section 2 is the review of the related works about transfer learning and metric learning. The formal definitions for the problem and the framework are presented in Section 3. In Section 4, comprehensive experiments on four types of real-world datasets verify the effectiveness and efficiency of the proposed methods. Finally, we conclude our work in Section 5. 2 Related work According to [19, 20], transfer learning methods can be roughly divided into feature matching [10, 12, 14, 15] and instance re-weighting approaches [21]. Feature matching methods are either by exploring cross-domain feature invariants in the reproducing kernel Hilbert space, or by leveraging knowledge from the source domain to the target domain via subspace alignment technology. While instance re-weighting approaches adjust the weight of samples in the source domain according to their relevance to the target instances so that the distribution difference between domains is reduced. Compared to instance re-weighting methods, feature matching methods can deal with large discrepancy of distributions between source and target domains. According to the existence of labelled data in the target domain, these methods can be roughly divided into semi-supervised and unsupervised situations [22]. It is worth noting that both of them need adequate unlabelled data in the target domain. Semi-supervised [23, 24] always assume that there are part of labelled data of target domain. Thus, labelled target data can be used to improve the tasks of target domain. Labelled instances are often time consuming, expensive, and even difficult to obtain, as they require the efforts of expert. Therefore, unsupervised domain adaptation is a more general scenario. The challenge of semi-supervised learning is how to introduce both labelled and unlabelled data within one formulation, which often leads to a quite complex optimisation problem with lots of hyper-parameters. Therefore, this paper focus on the latter situation since it is more general and still challenging. Recently, metric learning also has achieved success in various fields, including transfer learning [25, 26], face [27, 28], and kinship verification [29] and person re-identification problem [30]. The transfer metric learning method is proposed to learn a metric for the target task via exploiting the correlations between cross-domain tasks. In order to make knowledge transfer across domains more effective, [31] proposes a metric transfer learning framework to learn instance weights and a Mahalanobis distance in a parallel framework. It is noteworthy that these works require abundant side information or some labelled data in target domain, which is contrary to our hypothesis. 3 Unsupervised transfer metric learning In this section, we present in detail the Unsupervised Transfer Metric Learning (UTML) approach to obtain a robust transfer classifier. 3.1 Problem formulation Given a labelled source domain , and unlabelled target domain , where and are number of instances in source and target domain, respectively. This paper use subscripts s and t to indicate the source domain and the target domain, respectively. This article also assumes that the source domain and the target domain are homogeneous, that is, the feature space and the label space . Moreover, , here C means the number of data classes. Suppose samples are from C classes, we have . The goal of transfer learning is to get a more accurate classifiers for unlabelled target data learned from labelled source data. 3.2 Leveraging the discriminative information from target data The ultimate goal of unsupervised domain adaptation is to obtain a more accurate and robust classifier. Naturally, it is important to increase the discrimination interval of different classes and reduce the interval of same classes simultaneously. However, it requires sufficient labelled target data. Since labelled and unlabelled data are sampled from different distributions, the classifier trained on labelled source data would be not robust and efficient enough for target data. In fact, many works have already demonstrated this phenomenon and it is called as domain shift [10, 22, 32, 33]. Here, we utilise the pseudo-labels of target data which are predicted by the classifier trained on source domain to preserve the discriminative information from target data. Another key issue of domain adaptation is to find a suitable measure for describing differences in the distribution of the cross-domain data, such as the Maximum Mean Discrepancy (MMD) [17, 18], Kullback-Leibler (KL) divergence [34] etc. This paper chooses the former based on (i) The maximum mean discrepancy is a non-parameter measure method to avoid the non-trivial problems of parametric issues; (ii) Traditional method based on metric learning is very sensitive to the noise of the pseudo-label [26] or always require abundant-side information (i.e. the similarity or dissimilarity pairwise constraints over the labelled target data), but the target data addressed here is unlabelled. The maximum mean discrepancy is a measure based on instance means, hence is non-sensitive to label noise. That is, the differences of distribution and are empirical estimated by (1)where represents the reproducing kernel Hilbert space (RKHS), denote kernel function which maps the original samples to RKHS. If source data and target data are sampled from the same distribution, then the value of above equation will reduce to 0. 3.3 Minimising the intra-class distances There is no doubt that the mismatch of feature between the source domain and the target domain weakens the transfer ability. Through transformation matrix ( and k is the dimentionality of subspace), the source data and target data are project into feature invariant space where the differences are well reduced. Therefore, the projection matrix can be obtained via solving the minimisation of intra-class distance (2)where and denote the numbers of samples belonging to class c, respectively. indicates while indicates . For target data, the (pseudo) label can be obtain by a classifier trained in advance from source data. The above problems are equivalent to (3)where (4) 3.4 Maximising the inter-class distances However, reducing the intra-class distance does not guarantee that inter-class distances are far enough for classification. As shown in Fig. 1, if we want to get a robust transfer classifier for target data, it is also important to ensure the inter-class distances are increased so that different categories are separated by a large enough margin. Therefore, the instances means of different classes are formulated to be far from each other, and (5)where indicates that the sample belongs to category c. For class c, other classes are considered as a class. That is (6)formally speaking, (6) can be represented as (7)where and . In summary, for a certain class, we keep the means of other classes far away from it to increase the inter-class distance. 3.5 Optimisation problem In order to maintain the property of the data and avoid mapping to irrelevant dimensions, this paper also maintains the variance of the data. That is, the optimisation problem can be formulated as (8)where is the scatter matrix and is the centring matrix, is the column vector with all ones. Here, is the regularisation coefficient to avoid overfitting. According to above equation, the final optimisation problem can be (9)by further taking advantage of matrix tricks and regularisation, (9) can be formalised as: (10)Problem (10) is a constrained non-linear optimisation problem. If we denote as the Lagrange multiplier, it is not difficult to get the corresponding Lagrange function (11)By setting , we have (12)Clearly, the problem expressed in (12) is a typical generalised eigen-decomposition problem, which can be effectively and efficiently solved. Therefore, the projected matrix can be optimised by computing the k-smallest eigenvectors of (12). Finally, we summarise our method in Algorithm 1 (see Fig. 2). Fig. 2Open in figure viewerPowerPoint Algorithm 1: Unsupervised Metric Transfer Learning Algorithm 3.6 Computational complexity Big O notation is generally used to indicate time complexity of the algorithm. This paper analyses the computational complexity of Algorithm 1 (Fig. 2) by big O notation since it is simple but effective. Empirically, the dimension k is not >500, and the number of iterations is not >20, so , . The details of computational cost are: for solving the eigen-decomposition problem of (12), for constructing the and and for all other steps. In conclusion, the overall computational complexity is . The complexity can be greatly reduced by low-rank approximation and is similar to existing methods, i.e. JDA method. 4 Experiment 4.1 Datasets and details In this section, we conduct extensive experiments for image classification problems to evaluate these approaches. Office [http://www-scf.usc.edu/oqinggo/domainadaptation.html.], Caltech10 [http://www-scf.usc.edu/oqinggo/domainadaptation.html.], COIL20 [http://www.cs.columbia.edu/CAVE/software/softlib/coil-20.php.], PIE [http://www.cad.zju.edu.cn/home/dengcai/Data/MLData.html.], MNIST [http://www.ri.cmu.edu/research_project_detail.html?project_id=418&menu_id=261.] and USPS[http://www.ri.cmu.edu/research_project_detail.html?project_id=418&menu_id=261.] are widely used in evaluating the performance of transfer learning algorithms. The details information of these datasets are shown in Table 1 and Fig. 3. Table 1. Data information of the experiments Dataset Instances Feature Class Type Domain USPS 1,800 256 10 Digit U MNIST 2,000 256 10 Digit M Caltech 1,123 800 10 Object C Office 1,410 800 10 Object A, D, W COIL20 1,440 1,024 20 Object CO1, CO2 PIE 11,554 1,024 68 Face PIE1,PIE2,…,PIE5 Notation for datasets: U:USPS; M:MNIST; Caltech:C; Amazon:A; Webcam:W; DSLR:D; COIL1:CO1; COIL2:CO2. Fig. 3Open in figure viewerPowerPoint Image samples from Office, Caltech-256, USPS, MNIST, COIL20 and PIE (best viewed in colour online) Object Recognition1: Office and Caltech10 datasets contain 10 common categories such as backpack, bike, calculator, head-phones, keyboard, laptop, monitor, mouse, mug, and projector. These datasets contain images from four different domains: Amazon (images downloaded from online merchants), Webcam (low-resolution images by a web camera), DSLR (high resolution images by a digital Single Lens Reflex (SLR) camera), and Caltech-10 (selected the common classes from the Google images, Caltech-256). This paper utilises the SURF feature [35], which are encoded with 800-bin histograms. Another object recognition dataset is COIL20, which is divided into two parts: COIL1 contains all images taken in the directions of and COIL2 contains the remaining angle. Assuming that C is selected as the source domain and A is the target domain, the transfer task can be constructed as C→A. Face Recognition: The FIE dataset has 68 individuals with 41,368 face images of size 32 × 32, which are captured by 13 synchronised cameras (or understood as different poses) and 21 flashes (different illuminations and/or expressions). We chose the subset of them, and here PIE1, PIE2, PIE3, PIE4, PIE5 are, respectively, from left pose, upward pose, downward pose, frontal pose, and right pose. In each pose, all the face images are taken under different lighting, illumination, and expression conditions. Digit Recognition: MNIST and USPS are two widely used handwritten digital datasets. To accelerate the calculation process, 1800 images in USPS are randomly sampled and 2000 images in MNIST are selected to form the cross-domain recognition task. Meanwhile, all the input images of MNIST and USPS are normalised to a size of 16 × 16. 4.2 Implementation details Since labelled and unlabelled data are sampled from different distributions, it is impossible to tune the optimal parameters using cross-validation. We also compare our approach with several state-of-the-art methods for image classification problem, including 1-Nearest Neighbour Classifier (1NN), Principal Component Analysis (PCA), JDA, GFK, Transfer Component Analysis (TCA), and Joint Geometrical Statistical Alignment (JGSA). 1NN is trained on the labelled source data, and tested on the unlabelled target data. PCA method is a standard dimensionality reduction and is always compared to transfer learning methods to demonstrate the improvement of transfer learning method. TCA method, JDA method, GFK method, TJM method, and JGSA method are state-of-the-art transfer learning methods, which perform on source and target data as a dimensionality reduction procedure. Then, a 1NN classifier is trained on the labelled source data for classifying the unlabelled target data. We focus on the performance of the classifier on the target task, therefore, the calculation of the accuracy rate can be expressed as (13)All baseline methods are evaluated by empirically searching the optimal parameter settings from the parameter space, and then the best results are reported. For subspace learning method like GFK, base k is searched from , while for transfer learning methods, adaptation regularisation parameter is searched from and the best results are reported in Table 2. Figs. 4 and 5 show the sensitivity of different parameters. Fig. 6 shows the convergence of the four tasks in the iterative process. Table 2. Accuracy (%) on 4 types of cross-domain image datasets Dataset Baseline method Our method NN PCA TCA GFK JDA JGSA C→A(1) 23.70 36.95 38.20 46.03 44.78 53.13 46.03 51.46 51.67 C→W(2) 25.76 32.54 38.64 36.95 41.69 48.47 43.05 48.81 51.19 C→D(3) 25.48 38.22 41.40 40.76 45.22 48.41 47.77 51.59 54.78 A→C(4) 26.00 34.73 37.76 40.69 39.36 41.50 37.22 42.56 44.35 A→W(5) 29.83 35.59 37.63 36.95 37.97 45.08 38.98 47.80 47.80 A→D(6) 25.48 27.39 33.12 40.13 39.49 45.22 40.76 40.76 42.04 W→C(7) 19.86 26.36 29.30 24.76 31.17 33.57 29.47 35.17 36.95 W→A(8) 22.96 31.00 30.06 27.56 32.78 40.81 37.47 39.04 40.92 W→D(9) 59.24 77.07 87.26 85.35 89.17 88.54 78.34 87.26 91.08 D→C(10) 26.27 29.65 31.70 30.28 31.52 30.28 29.83 35.80 36.33 D→A(11) 28.50 32.05 32.15 32.05 33.09 38.73 31.00 36.95 36.74 D→W(12) 63.39 75.93 86.10 80.34 89.49 90.51 74.24 85.76 90.51 U→M(13) 44.70 44.95 51.05 46.25 59.65 68.15 60.20 51.65 59.75 M→U(14) 65.94 66.22 56.28 67.22 67.28 80.44 70.22 63.00 76.11 COIL1→2(15) 83.61 84.72 88.47 72.50 89.31 91.25 88.75 90.14 91.94 COIL2→1(16) 82.78 84.03 85.83 74.17 88.47 91.25 87.92 88.89 90.83 PIE1→PIE2(17) 26.09 24.80 40.76 26.15 68.57 62.25 68.69 73.97 80.42 PIE1→PIE3(18) 26.59 25.18 41.79 27.27 65.81 60.05 56.31 71.32 83.46 PIE1→PIE4(19) 30.67 29.26 59.63 31.15 90.99 80.56 90.90 86.03 96.55 PIE1→PIE5(20) 16.67 16.30 29.35 17.59 39.95 45.10 45.77 59.38 66.91 PIE2→PIE1(21) 24.49 24.22 41.81 25.24 72.66 68.25 67.29 58.82 79.35 PIE2→PIE3(22) 46.63 45.53 51.47 47.37 69.67 64.89 64.40 64.46 77.82 PIE2→PIE4(23) 54.07 53.35 64.73 54.25 84.65 77.56 83.15 84.02 92.55 PIE2→PIE5(24) 26.53 25.43 33.70 27.08 49.57 52.33 42.10 51.10 67.40 PIE3→PIE1(25) 21.37 20.95 34.69 21.82 56.06 62.91 60.20 55.07 75.90 PIE3→PIE2(26) 41.01 40.45 47.70 43.16 68.63 60.34 64.89 58.75 79.74 PIE3→PIE4(27) 46.53 46.14 56.23 46.41 86.33 70.98 83.54 82.49 93.00 PIE3→PIE5(28) 26.23 25.31 33.15 26.78 54.60 51.23 51.90 60.11 64.95 PIE4→PIE1(29) 32.95 31.96 55.64 34.24 92.02 84.39 89.62 87.70 94.39 PIE4→PIE2(30) 62.68 60.96 67.83 62.92 90.91 83.49 90.12 89.93 94.48 PIE4→PIE3(31) 73.22 72.18 75.86 73.35 91.12 80.76 90.20 88.97 93.38 PIE4→PIE5(32) 37.19 35.11 40.26 37.38 67.16 65.87 61.52 73.71 82.84 PIE5→PIE1(33) 18.49 18.85 26.98 20.35 52.28 53.51 47.75 64.29 74.04 PIE5→PIE2(34) 24.19 23.39 29.90 24.62 56.97 57.46 56.97 44.75 74.03 PIE5→PIE3(35) 28.31 27.21 29.90 28.49 61.15 54.29 59.38 61.09 72.67 PIE5→PIE4(36) 31.24 30.34 33.64 31.33 68.13 62.30 64.94 66.72 85.43 Average 34.76 33.84 44.75 41.36 62.44 62.05 60.45 63.31 71.45 Bold and italic numbers indicate the best and second best results. Fig. 4Open in figure viewerPowerPoint Dimentionality sensitivity study of UTML on different types of datasets Fig. 5Open in figure viewerPowerPoint Regularisation parameter sensitivity study of UTML on different types of datasets Fig. 6Open in figure viewerPowerPoint Convergence analysis of UTML on different types of datasets. The dashed lines indicate the best results of baseline, which is obtained by several iterations Furthermore, to illustrate the role of maximising inter-class distances and minimising intra-class distances, we designed three strategies: (i) UMTL1 denotes the minimisation of intra-class distances; (ii) UMTL2 denotes the maximisation of interclass distances; (iii) UMTL3 denotes the minimisation of intra-class distances and the maximisation of inter-class distances simultaneously. Our method involves only two model parameters: subspace bases k and regularisation parameter . In the comparative study, we set k = 100 and (i) for the face datasets, (ii) for the digit datasets, (iii) for the object datasets. The number of iterations of all tasks are set to 10 and kernel is chosen as linear kernel since it is simple and parameter-free. 4.3 Experiment results and discussion We conducted 36 transfer tasks to verify the effectiveness and efficiency of the proposed methods, and the experimental results are shown in Table 2. Our approaches achieve much better performance than the six baseline methods with statistical significance. The performance in 36 image classification tasks improves 9% compared to the best baseline method. The TCA method only considers the marginal distribution adaptation, therefore it is difficult to achieve satisfactory performance when conditional distribution differences is large. The JDA method not only considers adapting the marginal distribution in different domains, but also considers the conditional distribution which reduces the intra-class distance. However, this is not enough if the distance between classes is not sufficient for classification. The GFK method can perform well on office and Caltech10 datasets. This is partly due to the fact that the subspace dimension should be small enough to ensure different subspaces transit smoothly along the geodesic flow, which will lost a lot of the original characteristics of the data. The JGSA method reduces the shift between domains both statistically and geometrically and can better performance on the object datasets. However, it only considers the discriminative information of source data and thus cannot achieve optimal performance. The JGSA method considers the geometric information of the data and learns different mapping matrices for the source data and the target data, and thus can achieve better performance than our method on the handwritten digital datasets. Intuitively, discovering a more discriminative feature representation across domains is crucial. However, these methods do not take the target discriminative information into account since (most) target data is unlabelled. Furthermore, UMTL1 and UMTL2 also can achieve better performance than the baseline method. As the result reveals, the optimal strategy still needs to adjust the inter-class distance and intra-class distance simultaneously to achieve the more robust performance. 4.4 Parameter sensitivity and convergence analysis To validate that UMTL can achieve comparable results at a wide range of parameter values, this paper also analyses the parameter sensitivity on different types of datasets. The result is shown in Figs. 4 and 5, where the dashed lines show the best results of baseline. As shown in Fig. 4, our method can exceed the most advanced baseline method in a wide range and the dimension k can be typically selected as [30,100]. It is difficult to reconstruct the distinguishing information of human face in the subspace with a low dimension hence the performance of our method is poor compared to the best baseline method in a lower dimension. Similarly, regularisation parameters have a wide range of values that are advantageous over best baseline methods as shown in Fig. 5. As shown in Fig. 6, for the number of iteration T, the results on three tasks can be converged to the optimum value after several iterations. It shows the superiority of proposed method. For object datasets (W→D and COIL1→COIL2), the accuracy of the algorithm does not change after five iterations, which means the proposed method can converage fast. We check the time complexity by running all algorithms on the C→A dataset and show the results in Table 3. We conduct all the experiment on a 64-bit Windows with Core i7 and 16 GB RAM. The version of Matlab is 2017. Table 3 illustrates that our method is similar to most transfer learning methods, i.e. JGSA method and JDA method. Table 3. Time complexity of our method and all the baseline methods Methods Runtime, s Methods Runtime, s NN 5.2 PCA 3.0 TCA 30.8 GFK 4.6 JDA 42.3 JGSA 20.3 our 35.2 — — 5 Conclusion Here, we propose a transfer metric learning method that simultaneously maximises inter-class distance and minimises intra-class distance to obtain a more robust classifier for unsupervised domain adaptation. Previous works only consider the discriminative information of the source data, thus the optimal performance could not be achieved. Intuitively, discovering a more discriminative feature representation across domains is crucial. To leverage the discriminative information of target domain, we consider the pseudo-labels of target data and thus we can alternatingly improve the labelling quality until convergence. The experiments on 36 standard tasks fully illustrate the superiority of the proposed method. In the future, we plan to extend our idea to other challenging scenarios, i.e. the source and target data have different features and distributions. For instance, in a cross-modal application, the source domain and the target domain could be a text corpus and an image dataset, respectively. 6 Acknowledgments Research of Zhiheng Zhou was supported by National Key R&D Program of China (2018YF C0309400), National Natural Science Foundation of China(61871188, U1401252) and Fundamental Research Funds for the Central Universities SCUT (2017MS062). 7 References 1Busto P.P., and Gall J.: ‘ Open set domain adaptation’. IEEE Int. Conf. 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