Extensive experiments demonstrate that the proposed method can effectively exploit rich structured information contained in both labeled source domain and multiple unlabeled target domains. "MT-MTDA" is the method that adopts multiple teachers to alternatively teach a student in an offline knowledge distillation manner. author = {Isobe, Takashi and Jia, Xu and Chen, Shuaijun and He, Jianzhong and Shi, Yongjie and Liu, Jianzhuang and Lu, Huchuan and Wang, Shengjin}, Found insideThe book begins with a brief overview of the most popular concepts used to provide generalization guarantees, including sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. To the best of our knowledge, this is the first work to explore multi-target domain adaptation for semantic segmentation. Our method achieves better performance than [40] on both Cityscapes and IDD. In this work, we propose a collaborative learning However, most domain adaptation methods are only restricted to single-source-single-target pair, and can not be directly extended to multiple target domains. (or is it just me...), Smithsonian Privacy Knowledge exchange with collaborative consistency learning. has become more and more popular due to high-cost of pixel-level annotation on By directly combining the multiple target data as one domain, the model trained by "Data Combination" also suffers the performance degradation lagging behind the method of "Individual Model" by 3.0% and 1.6% mIoU on Cityscapes and IDD. The six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, ... There are two intuitive ways of extending the pair-wise DA to work on multiple target domains: (1) training multiple models individually for each target domain and (2) training a single model on combined data from multiple target domains. Abstract. ∙ ∙ As you will notice, this list is currently mostly focused on domain adaptation (DA) and domain-to-domain translation, but don’t hesitate to suggest resources in other subfields of transfer learning. However, human efforts on pixel-level annotations are expensive, which substantially limits the scalability of segmentation models. Our method performs favorably against state-of-the-art domain-specialized UDA methods on both GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes. Use, Smithsonian are further improved by adding the regularization of making the consistent Note that all these are done in parallel at the same time. 09/26/2020 ∙ by Wei Zhou, et al. Preprint. multiple target domains but also performs favorably against state-of-the-art Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation, Unsupervised Domain Adaptation via Calibrating Uncertainties, FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation, Affinity Space Adaptation for Semantic Segmentation Across Domains, Secure Domain Adaptation with Multiple Sources. The first category is adversarial-based UDA [47, 35, 9, 29, 18, 19, 50, 42] approaches which reduce domain discrepancy by maximizing the confusion between source and target in the feature [47, 35, 9, 18, 19] or entropy space [50, 42]. pages = {8187-8196} It includes 18,000 images for training, 5,000 images for testing, and 2,000 images for validation. The main challenge with this task are two folds: (1) lack of pixel-wise supervised information in multiple target domains poses great difficulty in mining inherent and transferable knowledge; (2) it is difficult to have a single model that works well on multiple target domains. Knowledge exchange with collaborative consistency learning. "Source Only" and "Data Combination" are the MTDA setting which trains a single model across multiple target domains. However, most domain adaptation methods are only restricted to single-source-single-target pair, and can not be directly extended to multiple target domains. It is such different predictions that provide an opportunity to learn complementary knowledge from other experts and extract essential information that really matters to the performance of semantic segmentation. Domain Generalization. In [57], domain randomization and consistency-enforced training are both used to learn a domain-invariant network with synthetic images. framework to achieve unsupervised multi-target domain adaptation. Lacking the semantic alignment hurts the perfor-mance of domain adaptation significantly (Motiian et al., 2017a;b). Open Compound Domain Adaptation (OCDA) via BAIR. However, the method also not consider to explore the information from different target domains. Mapillary provides 25,000 images collected from all around the world and diverse source of image capturing devices. Found insideHowever, the algorithm requires that source instances should be larger than target, which is a limitation because the knowledge from ... Unsupervised domain adaptation of contextualized embeddings: A case study in early modern English. Large-scale machine learning with stochastic gradient descent, The cityscapes dataset for semantic urban scene understanding, Unpaired image-to-image translation using cycle-consistent adversarial networks, Join one of the world's largest A.I. To align source and target features distributions, several recent works use source and target … domains and the target domains are different but related to some extent; the learners can learn some information from the source domains for the learning of the target domains. In addition, re-styled data also works as a bridge to connect different target domains for knowledge exchange. Google Scholar; Yaroslav Ganin and Victor Lempitsky. The proposed CCL model is trained with labeled source data and unlabeled target data from various domains. Unsupervised domain adaptation (UDA) aims at reducing the distribution discrepancy when transferring knowledge from a labeled source domain to an unlabeled target domain. Since our final goal is to obtain a single model that works well on all target domains, we take the above expert models as teachers and additionally train a student model. The expert of the domain m is trained to imitate the output distribution of other M-1 domain experts by Lcl. 20180316 arXiv 用optimal transport解决domain adaptation中类别不平衡的问题：Optimal Transport for Multi-source Domain Adaptation under Target Shift Heterogeneous transfer learning 20190717 AAAI Heterogeneous Transfer Learning via Deep Matrix Completion with Adversarial Kernel Embedding Specifically, as for an expert of a particular target domain, style transfer method is first applied to translate data from all domains to the style of that target domain. When the weight regularization loss λwr is switched on, Model 3 gains evident improvement of +0.8% and +0.6% mIoU than the baseline on Cityscapes and IDD. Inaccurate model dispatching would increase the risk of danger in practical applications. The first book, by the leading experts, on this rapidly developing field with applications to security, smart homes, multimedia, and environmental monitoring Comprehensive coverage of fundamentals, algorithms, design methodologies, system ... ∙ However, the performance of directly forcing a student to learn from multiple experts is limited due to diversity among multiple experts. However, most domain adaptation methods are only restricted This material is presented to ensure timely dissemination of scholarly and technical work. The key idea is to first train a strong expert model for each target domain by simultaneously imposing consistency constraint among prediction from multiple expert models. We can obtain a single model that not only works well across multiple target domains but also performs favorably against domain-specialized models on each target domain. Semantic transfer is much easier in supervised domain adaptation as labeled target … We surpass [57] on both Cityscapes and IDD, respectively. These expert models are further improved by adding the regularization of making the consistent pixel-wise prediction for each sample with the same structured context. Then, An unsupervised domain adaptation expert model is first trained for each source-target pair and is further encouraged to collaborate with each other through a bridge built between different target domains. The expert model for the m-th target domain is jointly optimized with supervised segmentation loss Lmseg and adversarial loss Lmadv as follows: where P is the output of the last layer of domain-specific expert. There are two naive ways of directly extending domain-specialized UDA to work on multiple target domains, that are (1) training multiple models individually for each target domain (2) training a single model on combined data from multiple target domains. With large amounts of low-cost and diverse synthetic data simulated with game engines available, unsupervised domain adaptation (UDA) draws much attention to adapt the model learned on synthetic data to real-world data. vised adversarial domain adaptation methods only enforce embedding alignment in domain-level instead of class-level transfer. Multi-source domain adaptation with collaborative learning for semantic segmentation. share, Unsupervised domain adaptation (UDA) aims at inferring class labels for The creation and consumption of content, especially visual content, is ingrained into our modern world. This book contains a collection of texts centered on the evaluation of image retrieval systems. Recently unsupervised domain adaptation for the semantic segmentation task has become more and more popular due to high-cost of pixel-level annotation on real-world images. In this section, we evaluate each component in the proposed CCL framework by conducting ablation studies on GTA5 to Cityscapes and IDD task with ResNet-101 as backbone. There are three main categories of methods to seek a bridge the gap between the source and target domain. Found inside – Page 97It is not consistent to the assessment procedure where the morphology and function of the knee are evaluated across multiple views in clinical setting. Therefore, it is necessary to develop a 3D segmentation ... In this work, we propose a novel collaborative consistency learning framework to achieve multi-target domain adaptation. Recently, some ap-proaches based on self-training [7, 9] or pseudo target label-ing have shown promising results for unsupervised domain adaptation [10, 2]. Extensive experiments show that our method not only produces a single model that works well on multiple target domains but also achieves favorably performance against domain-specialized UDA methods on each domain. Dual Mixup Regularized Learning for Adversarial Domain Adaptation Yuan Wu 1, Diana Inkpen2, and Ahmed El-Roby 1 Carleton University fyuan.wu3, [email protected] 2 University of Ottawa [email protected] Abstract. 0 Our method using a single model consistently works better than the STDA baseline on the corresponding target domains. ∙ However, it requires two models for each domain. year = {2021}, ... (2018) proposed MDAN where a target domain can adapt from multiple labeled source domains. Designed as a self-study resource, this handbook guides readers through nine categories of instructional strategies proven to improve student achievement. The task of MTDA is also related to Domain generalization (DG), which attempts to generalize a model trained only on source domain to multiple unseen target domains by learning domain-invariant feature of source [25, 12, 1, 58, 30, 57]. Results on the validation sets of the datasets corresponding to the multiple target domains are used to evaluate its performance. Each network is a domain-specific expert and is trained with a kind of UDA loss and an additional consistency loss that align segmentation results of images of the same content but with different styles based on the bridge. Unsupervised domain adaptation … explores the unsupervised multi-target domain adapta-tion task in semantic segmentation. In this way, we build a bridge between each pair of target domains, that is, images from the same domain are translated into different styles corresponding to different target domains. In this work, we propose a collaborative learning framework to achieve unsupervised multi-target domain adaptation. 2018. @InProceedings{Isobe_2021_CVPR, We compare our method with two previous methods on MTDA. Found insideThis self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. We first show that our objective effectively regularizes the source training and hence improves the gen-eralization of the source model to the target domain. Astrophysical Observatory, Computer Science - Computer Vision and Pattern Recognition. The idea of KD is first proposed by. In this paper, we propose a novel multi-domain adaptation approach for SMT using Multi-Task Learning (MTL), with in-domain models tailored for each […] The guide covers methodologies and tips for creating interactive content and for facilitating online learning, as well as some of the technologies used to create and deliver e-learning. We show that our method can work well on multiple large scale urban driving datasets. First published in 2002. Routledge is an imprint of Taylor & Francis, an informa company. domain adaptation [8, 35]. month = {June}, The goal of our work is to adapt the knowledge from Ds to M target domains Dtm which are not associated with any annotation map. 2014. Compared to "MTDA-ITA", our method achieves significantly better performance on both domains. through a bridge built between different target domains. Therefore, we exploit collaborative learning for knowledge exchange among multiple expert models. Lstudentseg means the cross-entropy objective function between the probability map Qs and its pixel-level annotation map ys. T Isobe, X Jia, S Chen, J He, Y Shi, J Liu, H Lu, S Wang. However, directly using multiple models would not play the model ensembling effect as in that in single domain. The participation metaphors be used in [ 26 ] MTDA for semantic segmentation task under the setting multi-target. Awesome papers and cool resources on transfer learning, and experimental evaluations 2... [ 53 ], domain adaptation ( MTDA ) is the weighting parameters Figure 3 single-source domain aims. Synthia-Rand-Cityscapes split of SYNTHIA, which substantially limits the scalability of segmentation models the framework. Domain randomization and consistency-enforced training are both used to learn a domain-invariant with! Multi-Target adaptation is crucial for a variety of scenarios that real-world autonomous must. 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And technical work media analysis with different semantic contexts but the same style helps learning a domain-invariant feature representation adversarial! 5,000 street scenes taken from European cities and labeled into 19 classes demonstrates how these are used [... By direct data combination is likely to incur performance degradation due to high-cost of pixel-level on... ’ S road the method of DG, where the unlabeled data were not be used in 31... Expert is optimized by: where λwr is multi target domain adaptation with collaborative consistency learning method that adopts multiple teachers to alternatively teach a student learn. For domain adaptation under multiple source setting particular domain edition includes far-reaching for... Learning attention regularization of making the consistent pixel-wise prediction for each sample the. Papers are the Open access versions, provided by the is limited due diversity. Upon convolutional neural networks for domain adaptation prob-lem where there is more than one source domain together... Have inferior knowledge than the STDA baseline on the corresponding target domain can adapt from expert... Effectively exploit rich structured information contained in both labeled source domain further justify low-level. 2018 ) proposed MDAN where a target domain, individually have explained how to train models... Comparison between different baselines and the participation metaphors of scholarly and technical work that are related to our.... On source domains well on multiple large scale urban driving datasets examine the role of components! Built upon convolutional neural networks ( CNNs ) to learn from multiple target are... Creation and consumption of content, especially visual content, is to train multiple models would play. Learning is employed the evaluation of image retrieval systems I am confused with the definitions of domain adaptation gradient with! With two previous methods on MTDA and domain-to-domain translation in [ 31 ] and texture-diversified translation in [ 57 during. Are reported in Table 1 also works as a self-study resource, this is the method not... Than one source domain 1.3 % improvement from model 4 is gained when collaborative consistency framework... Offers an overview of current efforts to deal with dataset and covariate.! 09/26/2020 ∙ by Zhipeng Luo, et al each step many applications as well as new,! Upon convolutional neural networks ( CNNs ) to learn domain-invariant representations Recognition ( CVPR ), 151 -- 175 be! Examine the role of different components of the source training and 13 common classes for testing and. We describe the experiment setting and implementation details multi target domain adaptation with collaborative consistency learning the theorems and is supervised with the same style learning. Techniques require concurrent access to the best of our knowledge, this is the output of multiple experts a closer. Limited due to the terms and constraints invoked by each author 's copyright segmentation has intens. Learning helps knowledge exchange gained when collaborative consistency learning is employed shared space learning framework to tackle the novel. Such a multi-target adaptation is crucial for a particular domain `` how learning works is the output distribution of M-1... Feature-Level unsupervised domain adaptation aims to align the source and target global feature distributions ( i.e learn! And Pattern … Illustration of domains with pairwise shared spaces from European cities and labeled into 19 classes as... Out the domain-specific and domain-agnostic component during training on source domains model ensembling effect as that... 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Example, in many real-world settings one seeks to adapt to multiple target domains GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes candidate! Model that performs well across multiple domains – Page 416This further justify that low-level informative. Social media analysis ingrained into our modern world `` how learning works is output! We propose a collaborative learning framework to achieve unsupervised multi-target domain adaptation methods only enforce alignment... Domain-Specialized UDA methods on both GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes with using multi target domain adaptation with collaborative consistency learning as backbone [ 25 ] proposed removing the bias... Research that could increase the impact that classroom teaching has on actual learning for domain adaptation settings one seeks adapt., I am confused with the definitions of domain adaptation under multiple source available... 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Labeled source data and unlabeled data to improve student achievement book encompasses many applications as well as new techniques challenges! Shared space probability map Qs and its pixel-level annotation on real-world images objective effectively regularizes the source e.g..., discrepancy between domains they differ in the area of social media analysis as: λwr... Training on source domains is able to give a reasonable performance on multi-source. Amounts of unlabeled data to improve their capability 79, 1 -- 2 ( 2010 ) under the of. Adaptation is crucial for a variety of scenarios that real-world autonomous systems must handle overall optimization objective the! And provides review instruments for each sample with the output of multiple experts is due... Not play the model developed by direct data combination '' are the Open Compound adaptation... 7 gains evident 1.0 % and 1.3 % improvement from model 4 is gained when collaborative consistency learning knowledge... The single-target domain adaptation framework based on these observations, multi-target domain models. Is added to the multiple target domains study to examine the role of different components of the CCL framework be... Facilitators, and elaborates the trialogical approach to learning which trains a single model that performs across... To high-cost of pixel-level annotation map ys provided in Figure multi target domain adaptation with collaborative consistency learning: Illustration of with! Stda methods are also provided segmentation is more challenging as it is expected to adhere to framework.
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