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Transfer Adaptation Learning: a Decade Survey JOURNAL OF LATEX CLASS FILES, VOL. -, NO. -, MARCH 2019 1 Transfer Adaptation Learning: A Decade Survey Lei Zhang, Xinbo Gao Abstract—The world we see is ever-changing and it always changes with people, things, and the environment. Domain is referred to as the state of the world at a certain moment. A research problem is characterized as transfer adaptation learning (TAL) when it needs knowledge correspondence between different moments/domains. Conventional machine learning aims to find a model with the minimum expected risk on test data by minimizing the regularized empirical risk on the training data, which, however, supposes that the training and test data share similar joint probability distribution. TAL aims to build models that can perform tasks of target domain by learning knowledge from a semantic related but distribution different source domain. It is an energetic research filed of increasing influence and importance, which is presenting a blowout publication trend. This paper surveys the advances of TAL methodologies in the past decade, and the technical challenges and essential problems of TAL have been observed and discussed with deep insights and new perspectives. Broader solutions of transfer adaptation learning being created by researchers are identified, i.e., instance re-weighting adaptation, feature adaptation, classifier adaptation, deep network adaptation and adversarial adaptation, which are beyond the early semi-supervised and unsupervised split. The survey helps researchers rapidly but comprehensively understand and identify the research foundation, research status, theoretical limitations, future challenges and under-studied issues (universality, interpretability, and credibility) to be broken in the field toward universal representation and safe applications in open-world scenarios. Index Terms—Transfer Learning, Domain Adaptation, Distribution Discrepancy, Computer Vision F 1 INTRODUCTION learning is no longer satisfied, which, therefore promotes the emergence of transfer learning (TL) and domain adaptation ISUAL understanding of an image or video is a long- (DA) [3], [4], [5]. Originally, the concept of transfer was first standing and challenging problem in computer vision. V proposed in 1991 by Pratt et al. [6] and further introduced Visual classification, as a fundamental problem of visual in [7] for transfer between neural networks. Mathematically, understanding, aims to recognize what an image depicts. A the earlier TL supposed different joint probability distribu- solidified route of visual classification is to establish a learn- tion, i.e., P (X ; Y ) 6= P (X ; Y ) between ing model based on a well-labeled image dataset, which source source target target source and target domains. DA supposed different marginal can be recognized as target data (task). However, labeling distribution, i.e., P (X ) 6= P (X ) but similar cat- a large number of target samples is cost-ineffective, because source target egory space between domains i.e., P (Y jX ) = it consumes a lot of human resources and time expenses in source source P (Y jX ). Currently, according to different set- labeling and becomes almost unrealistic for many domain target target tings of label set between domains, a preliminary split of specific tasks in real applications. Therefore, leveraging an- semi-supervised domain adaptation (SSDA), unsupervised other sufficiently labeled, distribution different but semantic domain adaptation (UDA), open-set domain adaptation related source domain for recognizing target samples is (OSDA) and partial domain adaptation (PDA) is presented, becoming an increasingly important topic. in all which the source labels are completely available. With the explosive increase of multi-source data from Notably, both SSDA and UDA assume the same category Internet such as YouTube and Flickr, a large number of space C across domain (close-set), i.e., C = C . The former labeled web database can be easily crawled. It is thus natural S T assumes a few labeled target samples can be used, while to consider train a learning model using multi-source web arXiv:1903.04687v2 [cs.CV] 22 Nov 2020 the target labels are completely unknown for the latter. data for recognizing target data. However, a prevailing Contrastively, OSDA and PDA assume the different category problem is that distribution mismatch and domain shift [1], space across domains, i.e. C 6= C . In general, OSDA [2] across source and target domain often exist owing to S T supposes C ⊂ C and PDA supposes C ⊂ C . In this various factors such as resolution, illumination, viewpoint, S T T S survey, we aim to cover the recent progress from a pure background, weather condition, etc. in computer vision. methodological perspective beyond the conventional split Therefore, the classification performance on the target task of semi-supervised, unsupervised, open-set and partial DA is dramatically degraded when the source data for training tasks. the classifier has different distribution from the target data. The earlier related reviews on transfer learning and This is due to that the fundamental independent identical domain adaptation can be referred to as [4], [15], [13], distribution (i.i.d.) condition implied in statistical machine [12], [10], [11], [14], [9], [8], [16], which are summarized in Table 1. However, our concern is that, these existing surveys • L. Zhang is with the School of Microelectronics and Communication can only reflect the several aspects in TL/DA community. Engineering, Chongqing University, Chongqing 400044, China. (E-mail: [email protected]). With the recent explosive increase of transfer learning and • X. Gao is with the Chongqing Key Laboratory of Image Cognition, domain adaptation models and algorithms, these surveys Chongqing University of Posts and Telecommunications, Chongqing are unable to accurately characterize her status, challenges 400065. (E-mail: [email protected]). and future for this community from a more comprehensive JOURNAL OF LATEX CLASS FILES, VOL. -, NO. -, MARCH 2019 2 TABLE 1 Source Tasks Target Tasks Summarization of Related Surveys in TL/DA F F No. Survey Title Ref. Year ? 1 A Survey on Transfer Learning [4] 2010 Extreme learning machine based transfer 2 [8] 2017 learning algorithms:A survey Fig. 1. Cross-domain object detection, recognition and semantic seg- 3 A survey of transfer learning [9] 2016 mentation. F denotes models of three tasks learned on source domain. Transfer Learning for Visual The value of TAL depends on the performance gain of target tasks. 4 [10] 2015 Categorization: A Survey A survey of transfer learning for collaborative 5 [11] 2016 recommendation with auxiliary data adversarial net (GAN) [36] and its variants [37], [38], [39], [40], [41] that aim to synthesize plausible images belonging Visual Domain Adaptation 6 [12] 2015 to target distribution from some source distribution (e.g., A survey of recent advances noise signal), can also be recognized as generalized transfer Transfer learning using computational 7 [13] 2015 learning techniques (e.g., style transfer tasks). Differently, intelligence: A survey conventional transfer learning approaches put emphasis on 8 Deep visual domain adaptation: A survey [14] 2015 the output knowledge adaptation (e.g., high-level features) Transfer learning for activity across source and target domains, while GANs focus on 9 [15] 2013 recognition: a survey input data adaptation (e.g., low-level image pixels) from Domain adaptation for visual source distribution to target distribution. Recently, image 10 [16] 2017 applications: A comprehensive survey pixel-level transfer has been intensively studied in image-to- image translation [42], [43], [44], [45], [46], style transfer [47], [48], [49] and target face synthesis (e.g., pose transfer vs. age transfer) [50], [51], [52], [53], [54], etc. perspective. Therefore, in this survey, we use a general name In this survey, we focus on elaborating the technical Transfer Adaptation Learning (TAL) for unifying both TLs and advances and remained challenges in model-driven transfer DAs from a broader perspective, and discuss the technical adaptation learning. Learning from multiple sources for advances, challenges and under-studied issues. In the past transferring or adapting to new target domains offers the decade, especially the recent five years, TAL was one of possibility of promoting model generalization and under- the most active areas in machine learning community, and standing the biological learning essence. Notably, transfer the goal of which is to narrow down the marginal and adaptation learning is similar but different from multi-task conditional distribution gap between source and target data, learning that resorts to the shared feature representations such that the labeled source data from one or more relevant or classifiers for multiple different tasks (e.g., detection vs. domains can be utilized for executing different learning retrieval) [55], [56], simultaneously. tasks in target domain, as illustrated in Fig. 1. The cross- In the past decade, a number of transfer learning and domain recognition, detection and segmentation tasks and domain adaptation approaches have been emerged by fol- challenges are highlighted. lowing the expected target error upper-bounds of domain Moving forward, deep learning (DL) techniques [17], adaptation that minimizes a convex combination of the [18], [19], [20] have recently become dominant and powerful source empirical risk, domain discrepancy and joint error algorithms in feature representation and
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