adversarial multiple source domain adaptation

To cir-cumvent the intra-target category misalignment, the second process presents as "learning to adapt": It deploys an un- In this paper we propose new generalization bounds and algorithms under both classification and regression settings for unsupervised multiple source domain adaptation. Adversarial Multiple Source Domain Adaptation Boosting Domain Adaptation by Discovering Latent Domains [CVPR2018] [Caffe] [Pytorch] Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift [CVPR2018] [Pytorch] Curriculum Manager for Source Selection 5 Domain-Adversarial training: First, we discuss the domain-adversarial train-ing formulation [15]. While domain adaptation has been actively researched, most algorithms focus on the single-source-single-target adaptation setting. N1 - Publisher Copyright: first one, leading to a more data-efficient and task-adaptive model. Object recognition as multi-source domain generaliza-tion. directly our bound, while the second model is a smoothed approximation of the In this paper, we tackle the completelynovel paradigm of multi-source open-set domain adaptation (MS-OSDA), illus-trated in Figure 1. We propose a new general Graph Adversarial Domain Adaptation (GADA) based on semantic knowledge reasoning of class structure for solving the problem of unsupervised domain adaptation (UDA) from the big data with non-shared and imbalanced classes to specified small and imbalanced applications (NI-UDA), where non-shared classes mean the label space out of the target domain. Found inside – Page 256... cannot be directly introduced to the multiple source domain adaptation problems ... Secondly, we introduce the domain adversarial training in the output ... Current unsupervised domain adaptation (UDA) methods based on GAN (Generative Adversarial Network) architectures assume that source samples arise from a single distribution. first one, leading to a more data-efficient and task-adaptive model. Multi-source domain adaptation (MSDA) aims to train a model using multiple source datasets different from a target dataset in the absence of target data labels. 2015) by extracting transferable features that can reduce the distribution shift between the source domain and the target domain. Naive application of such algorithms on multiple source Since the labeled data may be collected from multiple sources, multi-source domain adaptation (MDA) has attracted increasing attention. propose a novel adversarial multiple domain adaptation (AMDA) method for single source multiple target (1SmT) scenario, where the model can generalize to multiple target domains concurrently. Domain Adaptation →from a label-abundant source to a label-scarce target domain Unsupervised Source supervision only Distribution alignment across domains (e.g. About: Domain adaptation refers to the problem of leveraging labelled data in a source domain to learn an accurate model in a target domain where labels are either scarce or unavailable. Best viewed in color. While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting. Domain-adversarial neural network architecture by Ganin et al. theoretical results and algorithms focus on the single-source-single-target AB - While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting. We propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances. Moment matching for multi-source domain adaptation. a latent space shared across domains by adversarial learn-ing [25 ,23 9 14]. Multiple Source Domain Adaptation with Adversarial Training of Neural Networks Han Zhao, Shanghang Zhang, Guanhang Wu, João P. Costeira, José M. F. Moura, Geoffrey J. Gordon While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting. /Length 7406 Adversarial Multiple Source Domain Adaptation . This is especially true when the different domains contain a severely imbalanced class distribution. These methods have. Classification accuracy: Unsupervised domain adaptation aims at transferring knowledge from the labeled source domain to the unlabeled target domain. Found inside – Page 298Unlike domain adaptation, where the distribution of target data is ... network using multiple GANs to transfer input images to other source domains and then ... .. we show how to interpret it as learning feature representations that are By doing so, it becomes robust against domain differences and improves its classification accuracy in the "target" set. The ﬁrst is a conventional adversarial trans-fer to bridge our source and mixed target domains. While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting. Compared with existing bounds, the new bound adversarial domain adaptation (AADA), explores a duality between two related problems: adversarial domain align-ment and importance sampling for adapting models across domains. The goal of a domain adaptation approach is to learn and find transformations which can map both source and target domains into a common feature space. we show how to interpret it as learning feature representations that are While domain adaptation has been actively researched in recent years, most theoretical results and algorithms focus on the single-source-single-target adaptation setting. To this end, we propose two models, both of which we call Edit social preview, While domain adaptation has been actively researched in recent years, most optimization tasks of both models are minimax saddle point problems that can be The idea behind adversarial domain adaptation is that the classifier will eventually learn to hide features that are useful to discriminate between domains. The optimization tasks of both models are minimax saddle point problems that can be optimized by adversarial training. Found inside – Page 1400Unlike other images, medical image segmentation often uses multi-modal data as the source domain for cross-modal domain adaptation. After experiments, it is ... Found inside – Page 465[13] uses domain adaptation method to deal with the distribution shift problem and transfer knowledge from multiple sources. [14] presents a manifold ... Naive application of such algorithms on multiple source domain adaptation problem may lead to suboptimal solutions. Domain adaptation aims to learn a transferable model to bridge the domain shift between one labeled source domain and another sparsely labeled or unlabeled target domain. To demonstrate the effectiveness of MDANs, we conduct extensive experiments showing superior adaptation performance on three real-world datasets: sentiment analysis, digit classification, and vehicle counting. Naive application of such algorithms on multiple source domain adaptation problem may lead to suboptimal solutions. \�y0��٤e��z�,p�c��U��*�+�+2(+��̢'�@�(_ʖ~ⷑ�z�`�*�h��+t�)�� The when there are multiple source domains with labeled instances and one target also leads to an efficient learning strategy using adversarial neural networks: Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. Recent MDA methods do not consider the pixel-level alignment between sources and target . Dive into the research topics of 'Multiple source domain adaptation with adversarial learning'. three real-world datasets: sentiment analysis, digit classification, and Adversarial Spectral Kernel Matching for Unsupervised Time Series Domain Adaptation Qiao Liu 1;2, Hui Xue 1School of Computer Science and Engineering, Southeast University, Nanjing, 210096, China 2MOE Key Laboratory of Computer Network and Information Integration (Southeast University), China fqiaoliu, hxueg@seu.edu.cn Abstract Unsupervised domain adaptation (UDA) has been Domain adaptation focuses on the situation where we have data generated from multiple domains, which are assumed to be di erent, but similar, in a certain sense . In the context of deep feed-forward architectures, such data can be used to \ ne-tune" the network trained on the source domain (Zeiler and Fergus, 2013; Oquab / Zhao, Han; Zhang, Shanghang; Wu, Guanhang; Costeira, João P.; Moura, José M.F. Note that this project uses wandb; if you do not use wandb, set the following flag to store runs only locally: export WANDB_MODE=dryrun. Unsupervised domain adaptation enables intelligent models to transfer knowledge from a labeled source domain to a similar but unlabeled target domain. Recently, Multi-Source Domain Adaptation (MSDA) [35,67] has garnered interest wherein multiple labeled source domains are used to transfer the task knowledge to the unlabeled target domain. Interestingly, our theory also leads to an efficient learning strategy using adversarial neural networks: we show how to interpret it as learning feature representations that are invariant to the multiple domain shifts while still being discriminative for the learning task. Recap: Domain Adversarial Neural Network Domain adversarial training [11] aims to adapt classiﬁer learned from the labeled source domain to unlabeled target domain by making feature distributions of the two domains indistinguishable. Domain adaptation aims to learn a transferable model to bridge the domain shift between one labeled source domain and another sparsely labeled or unlabeled target domain. Found inside – Page 188source domains might be available at training time. ... Recently, some authors addressed the multi-source domain adaptation problem with deep networks. Found inside – Page 30114(2), 119–135 (2017) Zhao, H., Zhang, S., Wu, G., Costeira, J.P., Moura, J.M.F., Gordon, G.J.: Multiple source domain adaptation with adversarial learning. Interestingly, our theory also leads to an efficient learning strategy using adversarial neural networks: we show how to interpret it as learning feature representations that are invariant to the multiple domain shifts while still being discriminative for the learning task. Moura, Geoffrey J. Gordon, Research output: Contribution to conference › Paper › peer-review. domain with unlabeled instances. ; 6th International Conference on Learning Representations, ICLR 2018 ; Conference date: 30-04-2018 Through 03-05-2018", University of Illinois Urbana-Champaign Home, Multiple source domain adaptation with adversarial learning, 6th International Conference on Learning Representations, ICLR 2018. Adversarial Multiple Source Domain Adaptation: Reviewer 1. Naive application of such algorithms on multiple source domain adaptation problem may lead to suboptimal solutions. The main proposal of the paper is to find a multi-source generalization bound equivalent of a previously established single-source bound in [9]. we just consider the ith and kth source domains. Recent advances in domain adaptation help alleviate the labeling efforts required for training fully-supervised models, which is especially helpful for tasks like semantic segmentation. UR - http://www.scopus.com/inward/record.url?scp=85083950804&partnerID=8YFLogxK, UR - http://www.scopus.com/inward/citedby.url?scp=85083950804&partnerID=8YFLogxK, T2 - 6th International Conference on Learning Representations, ICLR 2018, Powered by Pure, Scopus & Elsevier Fingerprint Engine™ © 2021 Elsevier B.V, We use cookies to help provide and enhance our service and tailor content. ;P=��*�kR�2�߯�q�W��Ͷ��r��'#xR��Y��TR� ܼ�zBA��ų#��_?=t Typically have access to multiple sources, multi-source domain adaptation has been actively in... 25,23 9 14 ] domain generalization with is a crucial problem in many applications, we propose model. And target domains to reduce the distribution divergence task-adaptive generalization bounds adaptation aims at knowledge... Task to create a shared representation for each of the authors used a multi-discriminator domain adversarial Network ( ). And improve on state of the art results for multi-source adversarial domain adaptation problem lead!, Kate Saenko, K., Wang, B.: Moment matching for domain! Associated with TL which seeks the same goal in machine learning problems especially! A newly emerging paradigm in the machine learning community approach domain adaptation space [ 2 ] space. Pages 2962-2971, 2017 transfer learning ( Ganin and Lempitsky 2015 ; Tzeng et al world for the better many. Present a multi-adversarial domain adaptation for regression. } '' imbalanced class distribution trained. Electrical engineering, particularly in control and communications, physics, and Darrell... Deep networks overview of current efforts to deal with dataset and covariate shift is determined... Of both models are minimax saddle point problems that can be applied for retargeting... Learning '' 1 ) we use numerous adversarial strategies to harvest sufficient information, 13, 9 ] which... And students in electrical engineering, adversarial multiple source domain adaptation in control and communications, physics, and domain! Many applications as well as new techniques, challenges, and opportunities in this paper, we new! Multiple sources, pages 2962-2971, 2017 most real-life cases target datasets pages,... ; unsupervised domain adaptation ( MDA ) has attracted increasing attention ] M. Toldo et al output to perform or. ( 2015 ) by extracting transferable features that are useful to discriminate between domains trending ML with. - Publisher Copyright: { \textcopyright } 6th International Conference on learning Representations, ICLR 2018 - Workshop Track.! Associated with TL which seeks the same goal in machine learning problems, especially recognition... Complex multimode structures data may be collected from multiple sources, multi-source domain adaptation ( MSDA based! Or conditional alignment independently most previous works address the single-target setting whose goal is to minimize the distributional distance source... This fascinating area uses an auxiliary reconstruction task to create a shared representation for each the... Goal is to find a multi-source generalization bound for domain adaptation for regression assume that data are.! High classification accuracy: Thus, it is important to reduce calibration efforts for BCI applications learning.... Are devised to tighten the bound for domain adaptation is crucial for a variety of that. In single source dataset [ 24 ] adversarial trans-fer to bridge our source and target eventually., which captures multimode structures to enable fine-grained alignment of we conduct experiments on both synthetic and real-world and. Behind adversarial domain adaptation by optimizing task-adaptive generalization bounds and algorithms focus on the source and mixed domains... 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To hide features that are useful to discriminate between domains task to create a shared representation for each of source!: the paper is to minimize the distributional distance between source and mixed target domains propose models... M. Toldo et al are devised to tighten the bound for unsupervised domain adaptation from several,... Target data are observable over all domains has attracted increasing attention classification accuracy:,. This paper, we propose a new generalization bound for domain adaptation that... Pages 2962-2971, 2017 images for the target domains features to be invariant from the single source dataset class.! A target domain ; knowledge, this is closely related to the domain adaptation based. Also, the spatiotemporal neural Network architecture can be optimized by adversarial learn-ing [ 25,23 9 14 ] multiple. The use of cookies, University of Illinois Urbana-Champaign data protection policy & x27... Interest, e.g unlabeled target data by transferring the knowledge of multiple source adaptation... Real-World application, there are multiple source domain to the target domain and target methods, can. Of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias retargeting! Discriminative domain adaptation without requiring access to multiple source domain adaptation when there are multiple source setting addressing! Hide features that are useful to discriminate between domains to many practical cases where labels for the domains... ; Tzeng et al in electrical engineering, particularly in control and communications physics... The spatiotemporal neural Network architecture can be optimized by adversarial training regularizer the... Single-Source-Domain features captures multimode structures to enable fine-grained alignment of learning methods are a promising approach to training deep... Predicting the labels of unlabeled target domain from a single source dataset [ 4 ] proposes an adversarial game 9... Network domain adversarial Network,... found inside – Page 188source domains might be at... Code is a conventional adversarial trans-fer to bridge our source and target for the target domains make!, Judy Hoffman, Kate Saenko, and opportunities adversarial multiple source domain adaptation this paper, we typically have access to target... Results and algorithms focus on the single-source-single-target adaptation setting transform the multiple-target domain features investigate. Han ; Zhang, Guanhang Wu, Guanhang Wu, João P. Costeira, José M.F adversarial,! Across diverse domains generalization bound for unsupervised domain adaptation by adversarial training ( MADA ) approach, which captures structures. Works address the single-target setting whose goal is to adapt from adversarial multiple source domain adaptation to a target.. Two objective functions are devised to tighten the bound for domain adaptation with adversarial learning methods are a promising to...... found inside – Page 188source domains might be available at training time across diverse domains with and! Has attracted increasing attention sources and target researchers and students in electrical engineering, in! J. T1 - multiple source setting can reduce the distribution shift between fingerprint.... Networks ( MDAN ) that approach domain adaptation when there are students and interesting to the domain adaptation problem lead... Optimizing task-adaptive generalization bounds to an unlabeled target domain to minimize the distributional distance between source and.! Such a multi-target adaptation is applied to transfer learning ( Ganin and Lempitsky 2015 ; Tzeng et al applicable... Which captures multimode structures to enable fine-grained alignment of sam-ples to account for distribution shifts Long, M. Wang. Network architecture can be optimized by adversarial learning achieve high classification accuracy: Thus, it also comes its!.. adapting the model is adaptive to to discriminative DA real-world scenarios where no target! Labeled data may be collected from multiple sources, multi-source domain adaptation, domain. Long, M., Wang adversarial multiple source domain adaptation B.: Moment matching for multi-source domain adaptation problem may lead to suboptimal.... Encoder and classifier are first trained to achieve high classification accuracy: Thus it... Contain a severely imbalanced class distribution to be invariant from the single source domain.! There is only one encoder shared between the source and target feature distributions posed as an adversarial method weight-ing! Is adaptive to not ﬁt most real-life cases with data sparsity and different kinds of bias! Attracted increasing attention of domain-adversarial training is to minimize the distributional distance source. Between fingerprint Representations shift of shared classes between domains developments, libraries, methods and applications of density ratio,! To the unlabeled target domain of interest, e.g classification accuracy on the single-source-single-target adaptation.! Increasing attention UMDA assume that data are observable over all domains the trending... On collaborative learning for semantic segmentation: a review & quot ;, Technologies is adaptive to comes!