IROS2019国际学术会议论文集 0222

Visual Domain Adaptation Exploiting Confi dence Samples Song Tang1 Yunfeng Ji1 Jianzhi Lyu2 Jinpeng Mi2 Qingdu Li1and Jianwei Zhang2 Abstract Domain adaptation methods are used to address a problem in which train scenario source domain and test scenario target domain are different The existing methods mainly perform adaptation via reducing domain discrepancy from the view of a probability distribution However the idea of probability distribution matching always leads to a complex optimization process Thereby these methods are diffi cult to apply in some scenario like online application or fast perception in dynamic environments In this paper we propose a new and simple domain adaptation method that utilizes confi dence samples to facilitate the classifi er training on the target domain Here the confi dence samples are a subset of the target samples and they have very credibly predicted labels In order to detect the samples a Category Similarity Collaborative Representa tion CSCR is fi rst developed by which the raw labels of all target samples are predicted using the smallest projection error according to the law of category After this the confi dence score of the raw predicted labels is evaluated by the energy context information of CSCR Finally the target samples with a high confi dence score are selected Because of the linearity of CSCR our method avoids complex optimization for matching the probability distribution Empirical studies on a standard dataset demonstrate the advantages of our method I INTRODUCTION Recently as supervised learning methods like deep learning 1 have made breakthrough progress on various classic prob lems the question of how to transfer the gained knowledge to other fi elds that is related but different has received more and more attention At present this kind of work is classifi ed as transfer learning As a branch of transfer learning domain adaptation is a specifi c classifi cation problem where the labeled train set and the unlabeled test set have different probability distributions In the manner of domain adaptation the train and test set are specialized as source and target domain respectively The goal of domain adaptation is to carry out accurate classifi cation in the target domain by transferring the knowledge from the source domain Domain adaptation is a typical machine learning problem However it is also essential for robotics When the working environment of a robot is changed the domain adaptation method can help the robot to maintain its effective perception in the novel environment The key to solving the domain adaptation problem is to effectively transfer the domain knowledge which is presented 1 Song Tang Yunfeng Ji and Qingdu Li are with Institute of Machine Intelligence IMI University of Shanghai for Science and Technology Shanghai 20093 China Technical Aspects of Multimodal Systems Group TAMS Department of Informatics University of Hamburg Hamburg 22527 Germany Email li informatik uni hamburg de 2 Jianzhi Lyv Jinpeng Mi and Jianwei Zhang are with TAMS Department of Informatics University of Hamburg Hamburg 22527 Germany Domain knowledge transfer Training Source domain Target domain Confidence samples Selecting Classifier on target domain Fig 1 The main idea of the proposed method by transferring the domain knowledge from the source domain the target samples having high credibility predicted label i e the confi dence samples are detected by which a classifi er used in the target domain is trained by the label information from the source domain A naive way is to directly use the source label information to facilitate the classifi cation task in the target domain To this end the domain adaptation problem should be converted to a conventional classifi cation problem where the train set source domain and test set target domain have the same probability distribution That is the probability distribution gap between the source and target domain should be minimized as much as possible At present in the fi eld of domain adaptation most of the existing methods either deep learning based approaches 2 3 or non deep learning based approaches follow this idea of probability distribution match Although the methods achieve competitive results they still have a substantial limitation as they usually adopt complex optimization to implement the probability distribution match This property makes these methods diffi cult to apply to some scenarios For example online application in which the target samples arrived sequentially are diffi cult to estimate the probability distribution of target domain fast perception in a dynamical environment that needs to rapidly adapt classifi er by the samples mined continuously from the dynamical environment In order to solve the problem above this paper proposes a new and straightforward domain adaptation method Fig 1 presents the main idea of the proposed method that in order to train a classifi er for the target domain we fi nd proper target samples with a very credibly predicted label by transferring the domain knowledge from the source domain In this paper 2019 IEEE RSJ International Conference on Intelligent Robots and Systems IROS Macau China November 4 8 2019 978 1 7281 4003 2 19 31 00 2019 IEEE1173 these samples are called confi dence samples In the proposed method three steps are adopted to implement the transfer of domain knowledge First by using the labeled source samples all the target samples are encoded as a representation by which the labels are predicted using a classifi er The obtained predicted labels are called raw predicted labels Second the confi dence score of the raw predicted labels is evaluated by specifi c context information of the developed representation Third target samples with a high confi dence score are selected From the steps mentioned above it is not diffi cult to fi nd that to make the proposed method work it is crucial to design an appropriate representation This representation should simultaneously have the capability of class distinguishing and additional context information that can be used as the credibility evaluation criteria Existing representations like deep features only meet this requirement with diffi culty In order to solve this problem a linear representation named Category Similarity Collaborative Representation CSCR is developed This feature has two attributes On the one hand CSCR is a variant of the collaborative representation Thus by the smallest projection error according to the law of category CSCR can be used to generate a raw predicted label On the other hand by introducing a regularization of category similarity CSCR gets extra energy context information for details refer to section IV A The contributions of this paper are summarized as follows 1 A new domain adaptation method is proposed which is different from the previous methods which are based on distribution matching This method utilizes confi dence samples to facilitate the classifi cation task in the target domain 2 In order to detect the confi dence samples a new collaborative representation i e CSCR is developed This proposed feature introduces a new regularization based on the category similarity constraint that endows the obtained representation with extra energy context information The remainder of this paper is organized as follows Section 2 introduces the related work Section 3 presents the preliminary Section 4 presents the details of the proposed method Section 4 gives the experimental results and related analyses Section 5 presents our conclusion II RELATIVEWORKS The proposed framework is an unsupervised domain adaptation method and does not belong to the deep learning based domain adaptation methods such as adversarial based methods 4 5 generative adversarial based methods 6 Therefore our review does not focus on the methods in the manner of deep learning framework At present most methods are based on the idea of minimizing the probability distribution of the source and target domain This work can be roughly grouped into two categories The fi rst category assumes that the source and target samples can be projected into a specifi c common space of features where the feature distributions are similar D D1D2Dk x1 kx2k xikxnk k Dc Fig 2 Composition of the dictionary matrixD Specifi cally D1 D2 Dk Dc 1andDcarray one by one to formDwhereDkis a sub matrix in which theithcolumn vector i e xk i is the ithsource sample from the kthcategory Pan et al 7 invented a dimensionality reduction method to learn the transfer components that span the common features space Duan et al 8 proposed an adaptive SVM which adopts the maximum mean discrepancy to adapt the parameters of a classifi er Long et al 9 constructed a new share feature representation by jointly adapting both the marginal and conditional distribution in a procedure of principled dimensionality reduction Fernando et al 10 based on the Bregman divergence minimized the difference between the two domains by subspace alignment skill Zhang et al 11 performed geometrical and statistical alignment by label propagation The second category focuses on fi nding an appropriate transformation to map source samples into the target space where the feature distribution of source samples was close to target distribution Huang et al 12 proposed an instance level approach that re weighted the source samples to match the target distribution Gong et al 13 proposed a selection approach that took the source samples that satisfi ed target distribution as the landmarks Long et al 14 proposed a method that takes simultaneous feature matching and instance reweighting into consideration Although the two kinds of approaches above obtained competitive results they ignored to model the transfer process from the source domain to the target domain The third kind of methods attempts to solve the above problem from the point of view of a manifold These methods regard the two domains as separate points on a manifold and model the transferring process by using the geodesic fl ow between them Gopalan et al 15 constructed the transfer by interpolation of subspaces in the geodesic fl ow Gong et al 16 proposed a kernel based approach that constructs the geodesic fl ow kernel by integrating an infi nite number of subspaces into the geodesic fl ow Caseiro et al 17 proposed a variation method that carries out the subspace sampling on the Spline manifold Because of the ability to describe the transfer process these manifold based methods obtain good results III PRELIMINARY This section consists of two parts the problem formula tion of domain adaptation is fi rst presented and then the collaborative representation is briefl y introduced A Problem Formulation Suppose that there are source domain S and target domain T and the two domains have different probability distribution 1174 Context predicted label generation EncoderCSCR Module A Classifier E Energy context information Classifier C Raw predicted label Context predicted label Confidence score computation Module B Module C si is high yi zi zi li ri gi Confidence samples Source samples X Context predicted label generation EncoderCSCR Module A Classifier E Energy context information Classifier C Raw predicted label Context predicted label Confidence score computation Module B Module C si is high yi zi li ri gi Confidence samples Source samples X Fig 3 The pipeline of detecting the confi dence samples where Classifi er E stands for the classifi er based on the smallest projection error according to the law of category and Classifi er C stands for the classifi er based on the max energy according to the law of category The detection process is as follows an target sample yi is fi rst encoded to the CSCR representation zi then ziand its energy context information ri are respectively inputed into Classifi er E and Classifi er C to obtain the raw predicted label li and the context predicted label gi in the end yi s confi dence score si is computed based on li gi and yi is marked as a confi dence sample when siis high S includesnlabeled samples selected fromccategories where thekthcategory hasnksamples T includesmun labeled samples from the same categories All source and target samples have been mapped into a space of deep features by a pretrained deep model LetX x1 x2 xi xm wherexi RNbe the source samples The target samples are denoted as Y y1 y2 yi yn where yi RN The core task of domain adaptation is to obtain accurate classifi cation in the target domain using the domain knowl edge from the source domain B The Collaborative Representation For simplicity here we directly adopt the notations of the problem formulation and give the collaborative representation of any target sample yi The collaborative representation 18 is a typical dictio nary method LetDbe the dictionary matrix which is formed by arranging the source samples according to their category labels Namely D D1 D2 Dk Dc Dk xk 1 xk2 xki xknk wherexki is theithsample belonging to thekthcategory of the source domain For clarity the composition ofDis shown in Fig 2 For any target sampleyi its collaborative representation uiis obtained by solving the following problem ui argmin ui 1 2kyi Duik2 1 To differentiate from other features collaborative repre sentation has obvious geometric meaning From the view of manifold learning all the source samples can be regarded as points located on a low dimensional manifold embedded in a high dimensional space Moreover the samples from different categories should cluster on different areas of the manifold Thus the collaborative representation is a projection ofyi on this manifold In this paper this representation is called standard collaborative representation IV THE PROPOSED METHOD Briefl y the proposed method consists of two steps First the confi dence samples are detected After this a classifi er used in the target domain is trained by the selected confi dence samples Fig 3 presents the pipeline of determining whether any target sample i e yi is a confi dence sample The pipeline consists of the four following modules 1 Module A CSCR feature transformation In the module yiis encoded to the representation of CSCR denoted as zi utilizing the labeled source sample 2 Module B raw predicted label generation In the mod ule by the classifi er based on the smallest projection error according to the law of category the raw predicted label of yi denoted as li is obtained 3 Module C credibility evaluation which includes two components a Context predicted label generation Here the energy context information of thezi denoted asri is fi rst extracted Then the predicted label ofyi is obtained by the classifi er based on the max energy according to the law of category In this paper the obtained label is called context predicted label denoted as gi b Confi dence score computation Here the confi dence score of the raw predicted label denoted as si is obtained according to the coherency of the raw predicted labelliand the context predicted label gi 4 The module of the confi dence sample selection In this module yiwith labelli will be selected as a confi dence sample if its confi dence score siis high In the remainder of this section the details of the framework above are presented First we introduce the category similarity collaborative representation and its energy context information Second we presents how to get the raw predicted label and the context predicted label respectively Third the computation of the confi dence score is given A Category Similarity Collaborative Representation CSCR and Its Energy Context Information In order to give the collaborative representation extra context information a new regularization called category 1175 Projecting DkDcD1D2 CR Energy distribution Energy distribution Energy view Energy view Without category similarity constraint CSCR With category similarity constraint Fig 4 The motivation of the regularization of the category similarity constraint where CSCR and CR stand for category similarity collaborative representation and standard collaborative representation respectively similarity constraint is introduced into Eq 1 The motivation of the regularization is presented in Fig 4 When a target sample selected from thekthcategory is mapped on the manifold formed by the source samples the target sample should be expressed by the source samples from thekthcategory as much as possible Under this constraint in the projection vector the non zero elements with large value will be concentrated in positions that are the same as the positions of the source samples from thekthcategory in the dictionary matrixD Correspondingly if the projection vector is observed from the view of energy its energy will distribute to the positions corresponding to thekthcategory Obviously it is possible to predict the category label of any target sample according to the energy distribution of the projection vector In this paper this kind of distribution is called the energy context information In comparison without the category similarity constraint the standard collaborative representation does not have the energy context information Based on the motivation above we propose Category Similarity Collaborative Representation CSCR According to the composition of dictionaryD zis rewritten as z z1 zk zc Thus computation of the category similarity collaborative representation can boil down to solving the following optimization problem zi argmin zi 1 2 kyi Dzik2 c X k 1 okzk i 2 2 where the fi rst item represents the constraint of minimizing reconstruction error the second item introduces a regulariza tion of the category similarity constraint is a regularization factor andokis a regularization parameter which represents the overall similarity betweenyiand all the source samples from the kthcategory Given the similarity betweenyiand as all the source sam ples arevi v1 v2 vj vm wherevj h yi xj means the similarity betweenyiand thejthsource sampl