adversarial domain adaptation github

Here, the authors specifically consider the work of [1]. GitHub is where people build software. 03/2021, I accepted the invitation to serve as an Area Chair for NeurIPS 2021. My research interests include machine learning for image animation, video generation, generative adversarial networks and domain adaptation. 2.2 Domain Adaptation for Emotion Adversarial Based domain adapation Collaborative and adversarial network for unsupervised domain adaptation Maximum classifier discrepeancy for unsupervised domain adaptation Detach and adapt: Learning cross domain disentangled deep representation Learning from synthetic data: Addressing domain shift for semantic segmentation Unsupervised domain adaptation by backpropagation. February 2020 Yucheng's paper "Adaptive Iterative Attack towards Explainable Adversarial Robustness" was accepted by the journal of Pattern Recognition.... see all News IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019. The domain-dependent use of these parameters can be learned, e.g. The task associated to the domain adaptation itself is to detect a varying number of 2D landmarks per frame in the target domain. ages the adversarial domain adaptation (ADA) framework to introduce domain-invariance. These methods can be roughly separated into three groups depending on which level the adversarial training is introduced: feature-level [11,45], Video (requires Technion account) way to extend adversarial domain adaptation to regression tasks. We first learn a generative model for the class condi- 2.2 Domain Adaptation for Semantic Segmentation Domain adaptation (DA) has been applied to develop semantic segmentation models to ease the problem of data annotations, by aligning the feature or output pixel-wise class distributions between the source and the target images [5,6,9,25]. More than 65 million people use GitHub to discover, fork, and contribute to over 200 million projects. [43, 50, 22], these adversarial domain adaptation methods may still be constrained by two bottlenecks. In an image, there would be regions that can be adapted better, for instance, the foreground object may be similar in … • Hsu et al. Minimum Class Confusion (MCC ), a general loss function for Versatile Domain Adaptation (VDA) 1 É É MSPDA T S T 1 É É MTPDA T n Fig.1: Versatile Domain Adaptation (VDA) subsumes typical domain adap-tation scenarios: (1) Unsupervised Domain Adaptation (UDA); (2) Partial Do-main Adaptation (PDA); (3) Multi-Source Domain Adaptation (MSDA); (4) The Domain Adversarial Loss proposed in Domain-Adversarial Training of Neural Networks (ICML 2015) Domain adversarial loss measures the domain discrepancy through training a domain discriminator. In this setting, we propose an adversarial discriminator based approach. We propose Drop to … ... A PyTorch implementation for Adversarial Discriminative Domain Adaptation. PADA簡介 - Partial Adversarial Domain Adaptation 10 Dec GAN Dissection簡介 - Visualizing and Understanding Generative Adversarial Networks 04 Dec M2Det簡介 - A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network 20 Nov In conservative domain adaptation, where the classifier is trained to perform well on the source domain, VADA can be used to further constrain the hypothesis space by penalizing violations of the clustering assumption, thereby improving domain adversarial training. Top News. Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation. Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. I think its important to be able to convey information with varying levels of technicality. Adversarial discriminative domain adaptation. end-to-end deep learning framework, Partial Adversarial Domain Adaptation (PADA). If 2015 saw the birth of adversarial domain adaptation (with DANN 5) and 2016 the birth of GAN-based domain adaptation (with CoGAN 6 and DTN 2), 2017 has seen huge improvements and amazing results with these methods. Click on Before and After to see the captions generated before/after adaptation. Our model is able to transform the synthesized samples into the test domain while maintaining the data clusters associa-tions. Domain adaptation aims to alleviate the domain shift problem by aligning the feature distributions of the source and the target domain. Paul Magron. 04/2021, we are organising a Special Issue on weakly supervised representation learning @ Machine Learning, Welcome to contribute!. During the training stage, a pair of images from two domains are fed to the DS model. However, the realistic source data commonly possesses an underlying multi-mode Recent works on domain adaptation exploit adversarial training to obtain domain-invariant feature representations from the joint learning of feature extractor and domain discriminator networks. Xinghao Ding, Fujin He, Zhirui Lin, Yu Wang, Yue Huang*, Crowd Density Estimation using Fusion of Multi-layer Features, IEEE Trans. We demonstrate that scAdapt … ADA uses adversarial training to construct rep-resentations that are predictive for trigger iden-tification, but not predictive of the example’s domain. Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Mathieu Cord, Patrick Pérez. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2962–2971, 2017. AAAI 2019 A PAC-Bayesian approach for domain adaptation with specialization to linear classifiers. 06/15/2020 ∙ by Antoine de Mathelin, et al. lenge to cross-domain tasks [16]. The tutorial notebook can be viewed here. Since the labeled data may be collected from multiple sources, multi-source domain adaptation (MDA) has attracted increasing attention. We invite submissions on any aspect of adversarial robustness in real-world computer vision. The domain private and shared features are fed into domain converters to convert the data from one domain to another domain: ((f_{S\to T}) converts source data into target domain, (f_{T\to S}) converts target data into source domain). Konstantinos Drossos. Adversarial domain alignment (e.g. However, the realistic source data commonly possesses an underlying multi-mode make it difficult to align domains for effective learning. Our method can adapt the sentence style from source to target domain without the need of paired image-sentence training data in the target domain. Recent MDA methods do not consider the pixel-level alignment … Here we show the captions before and after domain adaptation for CUB, TGIF and Flickr30k. Download paper here codes. We introduce a novel approach to unsupervised and semi-supervised domain adaptation for semantic segmentation. Visual description of adversarial domain adaptation The growth in computational power and the rise of Deep Neural Networks (DNNs) have revolutionized the field of Natural Language Processing (NLP). • Generator and Discriminator play an adversarial game – Generator tries to generate data that can fool the Discriminator, while Discriminator tries to distinguish The conversion is learning through an adversarial learning procedure. Semi-supervised domain adaptation (SSDA) is a novel branch of machine learning that scarce labeled target examples are available, compared with unsupervised domain adaptation. With domain adversarial training, deep networks can learn disentangled and transferable features that effectively diminish the dataset shift between the source and target domains for knowledge transfer. Along this line, domain adaptation modules such as moment matching [6,7,8,9] and adversarial adaptation [10,11,12] have been embedded in deep networks to learn domain … Compared to prior convolutional and recurrent NN-based relation classification methods without domain adaptation, we achieve improvements as high as 30% in F1-score. Tutorial 8: Transfer learning and domain adaptation less than 1 minute read Transfer learning definition and contexts, fine-tuning pre-trained models, unsupervised domain adaptation via an adversarial approach. FCNs in the wild: Pixel-level adversarial and constraint-based adaptation. arXiv preprint arXiv:1812.01754, 2018. On the top, the asymmetric multi-task model is depicted, which consists of a detection model and a segmentation model (DS). Exploring object relation in mean teacher for cross-domain detection. •The discriminator is train by Noise-correcting Domain Discrimination, a kind of class-aware domain adversarial learning. Information-Theoretic Domain Adaptation under Severe Noise Conditions. [14] Wei Wang, Hao Wang, Zhiyong Ran, Ran He. The cross-domain discrepancy (domain shift) hinders the generalization of deep neural networks to work on different domain datasets. My works has been published in top computer vision and machine learning conferences. In WACV, 2020. ACM International Conference on Multimedia (ACM MM), 2020. On the one hand, data from other domains are often useful to improve the learning on the target domain; on the other hand, domain variance and hierarchical structure of documents from words, key phrases, sentences, paragraphs, etc. Adversarial learning has been embedded into deep networks to learn transferable representations for domain adaptation. Download link for dataset used to evaluate the … In our work entitled “Adversarial Unsupervised Domain Adaptation for Acoustic Scene Classification”, we present the first approach of domain adaptation for acoustic scene classification. Importance Weighted Adversarial Nets for Partial Domain Adaptation . To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. ( urr. The model can be [2] Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. arXiv 2016 Authors: Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, Wenjun Zhang In this work, we propose to promote adversarial domain adaptation with both pixel-level and feature-level domain mixup. Google Scholar; Xavier Glorot, Antoine Bordes, and Yoshua Bengio. [September, 2020] Our paper, Targeted Adversarial Perturbations for Monocular Depth Prediction accepted to Neural Information Processing Systems (NeurIPS 2020). Discovering and Incorporating Latent Target-Domains for Domain Adaptation Haoliang Li, Wen Li, Shiqi Wang. In this respository, we implmented our proposed Wasserstein adversarial domain adaptation (WADA) model for object recognition. 24. Adversarial Bipartite Graph Learning for Video Domain Adaptation. Our source code is available on Github1 and the Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan.Arxiv.. Abstract. Conditional Alignment Input Space Shared-Feature Space Source Dog + Target Dog Source Cat + Target Cat S. Zhang, H. Zhao et al. gear tooth crack level). Self-training and adversarial background regularization for unsupervised domain adaptive one-stage object detection. Text classification, in cross-domain setting, is a challenging task. CyCADA: Cycle-consistent adversarial domain adaptation. In our proposed method, the autonomous agent uses a domain adaptation technique to discover a mapping that can align the state-action spaces of the new environment to the one which was learned previously. Index Terms—Multi-source domain adaptation, Adversarial Training, Task-specific, Domain Clustering Despite the rapid developments in domain adaptation, most existing methods transfer knowledge from single source do-main to single target domain [1]–[3]. However, domain-adversarial learning only aligns feature distributions between domains but does not consider whether the target features are discriminative. Adversarial Training Regardless of whether the distri-bution alignment is done globally or locally, one common way to align source and target domain data distribution is via adversarial training [12]. In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. Deep Adversarial Attention Alignment forUnsupervised Domain Adaptation: the Benefit of Target Expectation Maximization. More concretely, we denote Sas our simulated domain, Tas our in vivo domain, x s and x t refer an Adversarial Domain Adaptation framework for ZSL that leverages a ZSL model to improve upon the classification. (FNWild) [6] yADA: ycle -consistent adversarial domain adaptation, IML 2018. ... Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.) Published in the 29th International Joint Conference on Artificial Intelligence, 2020. In this work, we address the task of unsupervised domain adaptation in semantic segmentation with losses based on the entropy of the pixel-wise predictions. Adversarial Domain Adaptation with Domain Mixup . Recent works on domain adaptation exploit adversarial training to obtain domain-invariant feature representations from the joint learning of feature extractor and domain discriminator networks. 4 Domain adaptation-based transfer learning using an adversarial network. •The discriminator is train by Noise-correcting Domain Discrimination, a kind of class-aware domain adversarial learning. Talks Oral presentation for Adversarial Domain Adaptation with Domain Mixup AAAI Conference on Artificial Intelligence, 2018. This blog post accompanies my first co-first-author publication, Variational Recurrent Adversarial Domain Adaptation, at ICLR (YAY!). ∙ The University of Queensland ∙ 6 ∙ share . Recently, remarkable progress has been made in learning transferable representation across domains. Margin-aware Adversarial Domain Adaptation with Optimal Transport, ICML 2020; A Swiss Army Knife for Minimax Optimal Transport, ICML 2020; Reviewing: ICML 2021; Some useful links: A list of links that I found useful during my PhD years: tools, GitHub repositories, dataset download links etc… Hosted on GitHub Pages — Theme by orderedlist Published in IEEE Conference on Computer Vision and Pattern Recognition, 2018. The … Domain Adversarial Reinforcement Learning for Partial Domain Adaptation Jin Chen, Xinxiao Wu , Member, IEEE, Lixin Duan, and Shenghua Gao , Member, IEEE Abstract—Partial domain adaptation aims to transfer knowl-edge from a label-rich source domain to a label-scarce target domain (i.e., the target categories are a subset of the source Adversarial networks were originally developed for image generation (Good-fellow et al., 2014; Makhzani et al., 2015; Sprin-genberg, 2015; Radford et al., 2015; Taigman et al., 2016), and were later applied to domain adaptation As illustrated in Figure 1(a), our approach mainly consists of two components: a two-level hierarchical conditional GAN model and a domain adaptation model. ∙ ENS Paris-Saclay ∙ 0 ∙ share . The success of deep neural networks (DNNs) is heavily dependent on the availability of labeled data. 3.1. AITL takes gene expression of patients and cell lines as the input, employs adversarial domain adaptation and multi-task learning to address these discrepancies, and predicts the drug response as the output. Solutions to closed set domain adaptation mainly fall into two categories: feature adaptation and generative model. Published in ACM International Conference on Multimedia 2019, 2019. Generalized Bound on the Expected Risk. However, domain discrepancy is considered to be directly minimized in existing solutions, which is difficult to achieve in practice. Closed Set Domain Adaptation Closed set domain adaptation focuses on mitigating the impact of the domain gap between source and target do-mains. where ()is the confusion matrix, is the pseudo-labels, and ℒ(pt,k)is a … Recent domain adaptation work tends to obtain a uniformed representation in an adversarial manner through joint learning of the domain discriminator and feature generator. • Kim et al. an Adversarial Domain Adaptation framework for ZSL that leverages a ZSL model to improve upon the classification. The domain adapta- To make effective use of these additional data so as to bridge the domain gap, one possible way is to generate adversarial examples. Adversarial domain adaptation has made remarkable advances in learning transferable representations for knowledge transfer across domains. Fig. ICML 2018 [Ganin 2015] Y. Ganin and V. Lempitsky. ICML 2015. adversarial domain adaptation, allowing us to effectively examine the different factors of variation between the exist-ing approaches and clearly view the similarities they each share. Recommended citation: Jing Zhang, Zewei Ding, Wanqing Li, Philip Ogunbona (2018). … where ()is the confusion matrix, is the pseudo-labels, and ℒ(pt,k)is a … using domain-guided dropout (Xiao et al., 2016), or based on prior knowledge about domain semantic relationships (Yang & Hospedales, 2015). Adversarial Dual Distinct Classifiers for Unsupervised Domain Adaptation. In addition to the adversarial domain adaptation framework, we also present an efficient deep pixel-to-pixel network for nucleus identification, which is more streamlined than typical computerized Ki-67 scoring methods that use a multistage image processing pipeline. We conclude this section with a discussion and comparison of our bounds with existing generalization bounds for multisource domain adaptation [8, 35]. arXiv preprint arXiv:1409.7495, 2014. Unsupervised domain adaptation by backpropagation. First, when data distributions embody complex multimodal structures, adversarial adaptation methods may fail to capture such multimodal structures for a discriminative alignment of … The method is named adversarial domain adaptation (3D-ADA) and is shown in Figure 1. ACM IMWUT 2020. Tutorial 8: Transfer learning and domain adaptation less than 1 minute read Transfer learning definition and contexts, fine-tuning pre-trained models, unsupervised domain adaptation via an adversarial approach. In Proc. More than 65 million people use GitHub to discover, fork, and contribute to over 200 million projects. for domain adaptation [3,4,5]. Adversarial Weighting for Domain Adaptation in Regression. Published in AAAI, 2020 (Oral). Recommended citation: Zhongyi Han, Xian-Jin Gui, Chaoran Cui, Yilong Yin, " Towards Accurate and Robust Domain Adaptation under Noisy Environments".the 29th International Joint Conference on Artificial Intelligence, 2020. scAdapt used both the labeled source and unlabeled target data to train an enhanced classifier, and aligned the labeled source centroid and pseudo-labeled target centroid to generate a joint embedding. domain adaptation in both classification and regression settings, one by a union bound argument and one using reduction from multiple source domains to single source domain. I also did internship in Snap Inc. and Google. However, domain adversarial methods render suboptimal performances since they attempt to match the distributions among the domains without considering the task at hand. [P40] Mathur A., Isopoussu A., Kawsar F., Berthouze N., Lane N.D. "FlexAdapt: Flexible Cycle-Consistent Adversarial Domain Adaptation". The general idea is to learn both class discriminative and domain invariant fea-tures, where the loss of the label predictor of the source In this paper, the authors tackle the problem of Domain Generalization: Given multiple source domains, the goal is to learn a joint aligned feature representation, hoping it would generalize to a new unseen target domain. adaptation model that can correctly predict the labels of a sample from the target domain trained on f(X i;Y i)gM i=1 and fX T g. 3 Multi-source Adversarial Domain Aggregation Network In this section, we introduce the proposed Multi-source Adversarial Domain Aggregation Network (MADAN) for semantic segmentation adaptation. Adversarial Learning in Vision and NLP Our approach closely relates to the idea of domain-adversarial training. Cross-domain sentiment classification aims to address the lack of massive amounts of labeled data. Published in Arxiv, 2017. Early DA approaches leverage source examples to learn on the target domain in various ways, e.g. Recommended citation: Shuang Li, Chi Harold Liu, Binhui Xie, Limin Su, Zhengming Ding, and Gao Huang. It requires no labeled data from the target domain, making it completely unsuper-vised. 2: The flowchart of the proposed weakly supervised adversarial domain adaptation. In conservative domain adaptation, where the classifier is trained to perform well on the source domain, VADA can be used to further constrain the hypothesis space by penalizing violations of the clustering assumption, thereby improving domain adversarial training. Material. Domain adaptation is essential to enable wide usage of deep learning based networkstrained using large labeled datasets. ... Adversarial Training of Cross-domain Image Captioner" in ICCV 2017. reinforcement-learning tensorflow policy-gradient image-captioning adversarial-networks ... Domain Adaptation using External Knowledge for Sentiment Analysis. [September, 2020] Our paper, Spatial Class Distribution Shift in Unsupervised Domain Adaptation: Local Alignment Comes to Rescue accepted to Asian Conference on Computer Vision (ACCV 2020). In … Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks @article{Bousmalis2017UnsupervisedPD, title={Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks}, author={Konstantinos Bousmalis and N. Silberman and David Dohan and D. Erhan and Dilip Krishnan}, journal={2017 IEEE … However, obtaining labeled data is a big challenge in many real-world problems. We adapt an adversarial learning approach for domain adaptation (from Ganin et al., 2016; Tzeng et al., 2017). First, we train the combination of a source feature extractor F s and a subtomogram classifier C using labeled subtomograms from the source domain D s CyCADA: Cycle-consistent adversarial domain adaptation. 2.1 Domain Adaptation The basic intuition behind our approach is to, simultaneously, learn both re-gressors for beamforming, as well as maps that allow us to transform simulated channel data into corresponding in vivo data, and vice versa. Adversarial Nets-based Domain Adaptation The works in [7, 24] apply a domain classifier on the general feed-forward models to form the adversarial nets-based domain adaptation methods. To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Radar Sensor and Human Sensing . Chaoqi Chen, Weiping Xie, Yi Wen, Yue Huang*, Xinghao Ding, Multiple-Source Domain Adaptation with Generative Adversarial Nets, Knowledge based systems, accepted. [13] Lingxiao Song, Man Zhang, Xiang Wu, Ran He. An Adversarial Approach to Discriminative Modality Distillation for Remote Sensing Image Classification. 2. The tutorial notebook can be viewed here. The Overflow Blog Level Up: Creative Coding with p5.js – … A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation Jian Liang1[0000 0003 3890 1894], Yunbo Wang2[0000 0002 6215 8888], Dapeng Hu1, Ran He3[0000 0002 3807 991X], and Jiashi Feng1[0000 0001 6843 0064] 1 Department of ECE, National University of Singapore (NUS) liangjian92@gmail.com, dapeng.hu@u.nus.edu, elefjia@nus.edu.sg Domain adaptation for large-scale sentiment classification: A deep learning approach. Towards Accurate and Robust Domain Adaptation under Noisy Environments . We show that ADDA is more effective yet considerably simpler than competing domain-adversarial methods, and demonstrate the promise of our approach by exceeding state-of-the-art unsupervised adaptation results on standard cross-domain digit classification tasks and a new more difficult cross-modality object classification task. Unlike many earlier methods that rely on adversarial learning for feature alignment, we leverage contrastive learning to bridge the domain gap by aligning the features of structurally similar label patches across domains. DADA: Depth-aware Domain Adaptation in Semantic Segmentation. Tuomas Virtanen. read more as Hierarchical Generative Adversarial Networks (HiGAN) to transfer knowledge from images to videos by learning domain-invariant feature representations between them. The goal is to reduce the considerable domain gap between simulation and intraoperative cases, e.g. 3.When the network combines domain adver-sarial training with semi-supervised learning, we get further gains ranging from 5% to 7% absolute in F1 across events. 2.Domain adaptation with adversarial training improves over the adaptation baseline (i.e., a transfer model) by 1:8% to 4:1% absolute F1. We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Domain Adversarial Neural Network (DANN)¶ class dalib.adaptation.dann.DomainAdversarialLoss (domain_discriminator, reduction='mean', grl=None) [source] ¶. Joint Adversarial Domain Adaptation. The domain adaptation method uses a source-labeled dataset and a target-unlabeled dataset as the inputs, and the output is a deep convolutional neural network that maps vibration signals into corresponding health conditions (e.g. 논문 : Adversarial Discriminative Domain Adaptation - y2017,c1875 분류 : Unsupervised Domain Adaptation 저자 : Eric Tzeng, Judy Hoffman, Kate Saenko (California, Stanford, Boston University) 읽는 배경 : (citation step1) Open Componunt Domain Adaptation에서 Equ (1), (2) [the domain-confusion loss]가 이해가 안되서 읽는 논문. 02/2021, we are organising the first Australia-Japan Workshop on Machine Learning.. 02/2021, I was an Expert Reviewer for ICML 2021. %0 Conference Paper %T CyCADA: Cycle-Consistent Adversarial Domain Adaptation %A Judy Hoffman %A Eric Tzeng %A Taesung Park %A Jun-Yan Zhu %A Phillip Isola %A Kate Saenko %A Alexei Efros %A Trevor Darrell %B Proceedings of the 35th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jennifer Dy %E Andreas Krause %F pmlr … It demands to predict sentiment polarity on a target domain utilizing a classifier learned from a source domain. 2: The flowchart of the proposed weakly supervised adversarial domain adaptation. [4] Zhang et al., urriculum domain adaptation for segmentation of urban scenes, IV 2017. 2018. The adapted representa- tions often do not capture pixel-level domain shifts that are crucial for dense prediction tasks (e.g., semantic segmenta- tion). Our main contributions are summarized as follows: (1) We present an adversarial style mining (ASM) method to solve One-Shot Unsupervised Domain Adaptation (OSUDA) problems. On the top, the asymmetric multi-task model is depicted, which consists of a detection model and a segmentation model (DS). Unsupervised Domain Adaptation (UDA) approaches have frequently utilised adversarial training between the source and target domains. Doppler Based Detection of Multiple Targets in Passive Wi-Fi Radar using Underdetermined Blind Source Separation Unsupervised domain adaptation by backpropagation. Pascal Germain, Amaury Habrard, François Laviolette, and Emilie Morvant. Domain Adaptation. Index Terms—Multi-source domain adaptation, Adversarial Training, Task-specific, Domain Clustering Despite the rapid developments in domain adaptation, most existing methods transfer knowledge from single source do-main to single target domain [1]–[3]. Adversarial learning based techniques have showntheir utility towards solving this problem using a discriminator that ensures source andtarget distributions are close. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. set domain adaptation, partial domain adaptation, or open set domain adaptation. inatively trained Cycle-Consistent Adversarial Domain Adaptation (CyCADA) model. While adversarial learning strengthens the feature transferability which the community focuses on, its impact on the feature discriminability has not been fully explored. Similar to generative adversarial NNs (Goodfellow et al., 2014) (GAN), adversarial losses (Ganin and Lempitsky, 2015; Ganin et al., 2016) have been explored for domain adaptation. In CVPR, 2019. Confusion matrix: measure the difference between ground truth and pseudo-label. Domain adaptation is a long studied prob-lem,whereapproachesrangefromfine-tuningnetworkswithtarget data [28] to adversarial domain adaptation methods [31].

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