Recently, Semi-supervised Domain Adaptation (SSDA) has become more practical because a small number of labeled target samples can significantly boost the empirical target performance when using SSDA. Several current methods focus on prototype-based alignment to achieve cross-domain invariance in which the labeled samples from the source and target domains are concatenated to estimate the prototypes. The model is then trained to assign the unlabeled target data to the prototype within the same class. However, such methods fail to exploit the advantage of using few labeled target data because the labeled source data dominate the prototypes in the supervision process. Moreover, a recent method [1] showed that concatenating source and target samples for training can damage the semantic information of representations, which degrades the trained model's ability to generate discriminative features. To solve these problems, in this paper, we divide labeled source and target samples into two subgroups for training. One group includes a large number of labeled source samples, and the other obtains a few labeled target samples. Then, we propose a novel SSDA framework that consists of two models. A model trained on the group that has the labeled source samples to provide an "inter-view" on the unlabeled target data is called the inter-view model. A model trained on a few labeled target samples that provides an "intra-view" of the unlabeled target data is called the intra-view model. Finally, both of these models collaborate to fully exploit information on the unlabeled target data. To the best of our knowledge, our proposed method achieves the state-of-the-art classification performance of SSDA in extensive experiments conducted on several visual benchmark domain adaptation datasets that utilize the advantages of multiple views and collaborative training.
Semi-supervised domain adaptation (SSDA) is a promising technique for various applications. It can transfer knowledge learned from a source domain having high-density labeled samples to a target domain having limited labeled samples. Several previous works have attempted to reduce the distribution discrepancy between source domain and target domain by using adversarial-based or entropybased methods. These works have improved the performance of SSDA. However, there are still lacunae in producing class-wise domain-invariant features, which impair the improvement of the classification accuracy in the target domain. We propose a novel mapping function using explicit class-wise matching that can make a better decision boundary in the embedding space for superior classification accuracy in the target domain. In general, in a target domain with low-density label samples, it is more challenging to create a well-organized distribution for the classification than in a source domain where rich label information is available. In our mapping function, a representative vector of each class in the embedding spaces of the source and target domains is derived and aligned by using class-wise matching. It is observed that the distribution in the embedding space of the source domain can be effectively reproduced in the target domain. Our method achieves outstanding accuracy of classification in the target domain compared with previous works on the Office-31, Office-Home, Visda2017 and DomainNet datasets. INDEX TERMSSemi-supervised learning, domain adaptation, classification, transfer learning, mapping function. BA HUNG NGO (Student Member, IEEE) received a B.S degree in control engineering and automation from Hanoi University of Mining and Geology, VietNam, in 2014, and an M.S degree in control engineering and automation from Hanoi University of Science and Technology, in 2016. He is currently pursuing a Ph.D. degree at Dongguk University, Rep. of Korea. His current research interests include computer vision and deep learning, especially deep transfer learning, domain adaptation, and deep learning in medical imaging. JAE HYEON PARK (S'18) received a B.S. in Electronic Engineering from Daegu University, Rep. of Korea, in 2019 and is currently pursuing an M.S. degree at Dongguk University, Rep. of Korea, Seoul. His current research interests include image analysis and enhancement, tone mapping processing, deep learning classification, panel defects evaluation. SO JEONG PARK received a B.S. in Multimedia Engineering from Dongguk University, Rep. of Korea, in 2021 and is currently pursuing an M.S. degree at Dongguk University, Rep. of Korea, Seoul. Her current research interests include semantic segmentation and domain adaptation.
Multi-source Unsupervised Domain Adaptation (MUDA) is an approach aiming to transfer the knowledge obtained from multiple labeled source domains to an unlabeled target domain. In this paper, we propose a novel self-training method for MUDA, which includes pseudo label-oriented coteaching and pseudo label decoupling that are attempted for the pseudo label rectification-based MUDA for semantic segmentation. Existing ensemble-based self-training methods which are well-known approaches for MUDA use pseudo labels made from the ensemble of the predictions of multiple models to transfer the knowledge of source domains to the target domain. In these methods, information from multiple models can be contaminated, or errors from incorrect pseudo labels can be propagated. On the other hand, the proposed pseudo label-oriented coteaching trains multiple models by using pseudo labels from the peer model without any integration of pseudo labels. Simultaneously, the pseudo label decoupling method is proposed for rectification of pseudo labels, which updates the models with two pseudo labels only if they disagree. It also alleviates the problem of class imbalance in semantic segmentation, in which dominant classes lead the update for training. The effects of the proposed pseudo label-oriented coteaching and pseudo label decoupling on the performance of semantic segmentation were verified by extensive experiments. The proposed method achieved the best semantic segmentation accuracy compared with the benchmark methods. In addition, we confirmed that the prediction accuracy of small objects was greatly improved by the proposed pseudo label rectification.
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