{"content":[{"text":"Translation. However, the qualities of machine-generated \n data sometimes fail to meet the requirement. For example, \n machine-translated sentences sometimes cannot accurately \n express the meaning of the original sentences and even can- \n not be sentenced. Bridging two domains with such rough \n IC data cannot achieve satisfactory results. Moreover, the \n quality of data should be judged by a ﬂoating score rather \n than just good or bad. Although there are existing instance \n weighting strategies, such as reweighting regularizer (Long \n et al. 2014) and kernel mean matching (Chu, La Torre, \n and Cohn 2013), they are not designed to reweight IC data \n and can only be applied for HoTL. Some coping strategies \n were designed, such as selecting high-quality correspon- \n dence with active learning (Zhou et al. 2016). However, they \n fail to assign precise weights to IC data and waste massive \n information. \n To assign solid weights to IC data and learn more ro- \n bust transformed features for both domains, in this paper, \n we propose a novel HeTL method named Transfer Learn- \n ing with Weighted Correspondence (TLWC). Following a \n meta-learning paradigm (Finn, Abbeel, and Levine 2017; \n Ren et al. 2018), TLWC establishes a meta-learner to opti- \n mize the weights of IC data. The meta-objective of the meta- \n learner is to minimize the classiﬁcation loss of limited la- \n beled target-domain data. After the optimization is ﬁnished, \n weighted IC data are utilized to learn feature transforma- \n tions that map the source-domain data to the target-domain. \n Then the transformations can be optimized by the meta- \n learner further. After the optimization procedure is ﬁnished, \n the classiﬁer in the meta-learner is the one we need. The as- \n sumption we base is: If the IC data are assigned with the op- \n timal weights, the feature transformations learned with them \n is most adaptive for the classiﬁcation task in the target do- \n main. The framework of TLWC is shown in Figure 1. \n The contributions of the paper are summarized as fol- \n lows: (1) We propose a novel heterogeneous transfer learn- \n ing method, where the feature spaces of the two domains \n are uniﬁed with weighted IC data. Our method can be ap- \n plied to different features, datasets, and modalities. (2) We \n are among the ﬁrst to consider the IC data weights and we \n establish a meta-learner that utilizes the classiﬁcation loss of \n target-domain data to guide the weights updating and trans- \n formation optimization procedure. Compared with previous \n HeTL methods based on IC data, TLWC can effective re- \n move noisy information in IC data and learn a uniﬁed fea- \n ture space which is more suitable for the classiﬁcation task \n in the target domain.","bbox":{"left":0.088235,"top":0.069138,"width":0.38973,"height":0.654208}},{"text":"where the domain shift can be reduced by directly mini- \n mizing the data distribution discrepancy (Tzeng et al. 2015; \n Long et al. 2017), HeTL must unify the feature spaces of the \n two domains. For HeTL, there are two groups of methods for \n feature uniﬁcation: (1) Projecting data from the two domains \n into a common feature space (Wang and Mahadevan 2011; \n Hoffman et al. 2014; Yang et al. 2015; Herath, Harandi, and \n Porikli 2017); (2) Mapping data from one domain to another \n one (Li et al. 2013; Hoffman et al. 2014; Xiao and Guo 2015; \n Tsai, Yeh, and Wang 2016). \n For the ﬁrst group, for example, Li et al. (2013) pro- \n posed a semi-supervised heterogeneous domain adaptation \n (SFHA), which transforms data from two domains into an \n augmented feature space. Herath et al. (2017) considered an \n invariant latent space (ILS) and they established a common \n Hilbert space for both domains. \n For the second group, for example, Tsai et al. (2016) \n mapped the labeled source-domain data to the dimension- \n reduced target domain. And the mapped data are as- \n signed with different weights. Hoffman et al. (2014) pro- \n posed a max-margin domain transformation (MMDT). With \n MMDT, the data in the target domain are projected to the \n source domain, while the projected data are classiﬁed by \n maximizing the margins. \n The above HeTL methods bridge two domains by match- \n ing either the marginal or conditional distributions at \n the domain-level and the category-level respectively. With \n instance-correspondence (IC) data, the two domains can be \n bridged at the instance-level, which will boost the uniﬁca- \n tion and adaptation of the two heterogeneous domains. Ac- \n cording to the above two groups of feature uniﬁcation meth- \n ods, IC data can be utilized in various ways. For the ﬁrst \n group of methods, which learns a common feature space \n for the two domains, IC data can be taken as the equiva- \n lent data in the common feature space. For example, Xiao \n and Guo (2013) proposed a two-step learning method which \n turns feature space learning into a matrix completion prob- \n lem. The combined IC data are complete data in the matrix \n and they are employed to complete the unobserved data in \n the matrix. Zhou et al. (2015) designed a subspace for both \n domains and the projected IC data in the subspace are the \n same. For the second group of methods, which maps data \n from one domain to another, IC data are applied to learn \n the feature transformations. For example, Zhou et al. (2014) \n proposed a hybrid heterogeneous transfer learning (HHTL), \n which learns several transformation matrices with high-level \n features of IC data. Wang et al. (2018) applied deep autoen- \n coder to embed the shallow representations of both domains \n and learned a feature transformation with IC data features at \n the top embedding layer. \n However, all of the above methods consider the IC data \n to be equally important, which is not reliable. The IC data \n with low quality will hamper the domain adaptation proce- \n dure. Although there are existing instance weighting strate- \n gies (Chu, La Torre, and Cohn 2013; Long et al. 2014), they \n are only designed for HoTL. Zhou et al. (2016) selectively \n labeled IC data with active learning, but manually labeling \n IC data is prohibitive and informative machine-generated \n data are given up.","bbox":{"left":0.522058,"top":0.069134,"width":0.389717,"height":0.8207369999999999}},{"text":"Related Work \n Transfer learning or domain adaptation aims at transferring \n rich label information from a source domain to a target do- \n main. The main problem of transfer learning is how to re- \n duce the data discrepancy between the two domains (Pan \n and Yang 2010; Weiss, Khoshgoftaar, and Wang 2016). \n Depending on whether the feature spaces of the two do- \n mains are the same, transfer learning is divided into two \n categories: homogeneous transfer learning (HoTL) and het- \n erogeneous transfer learning (HeTL). Different from HoTL,","bbox":{"left":0.088235,"top":0.738435,"width":0.389713,"height":0.151459}},{"text":"4100","bbox":{"left":0.485935,"top":0.912903,"width":0.02812999999999999,"height":0.015271999999999952}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}