Showing 1-25 of 2,294 resultsfor "Index Terms":Unlabeled Target Domain
"Index Terms":Unlabeled Target Domain
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A Novel Deep Multi-Source Domain Adaptation Framework for Remaining Useful Life Prediction Across Operating Condition
2023 Global Reliability and Prognostics and Health Management Conference (PHM-Hangzhou)
Year: 2023 | Conference Paper |
In recent years, there has been a rapid development of data-driven domain adaptive remaining useful life prediction methods. It can more effectively predict training (source) and testing (target) condition monitoring data with different distributions. In practice, the target domain is not correlated with only one source domain, and label data can be obtained from multiple diverse domains, which le...Show More
Adapting Intra-Class Variations For Sar Image Classification
2021 IEEE International Conference on Image Processing (ICIP)
Year: 2021 | Conference Paper |
Cited by: Papers (3)
This paper presents a semi-supervised domain adaptation (SSDA) method for Synthetic Aperture Radar (SAR) image classification. SAR imagery is important in ground activity monitoring, but its wide application is impeded due to a lack of annotations. SSDA methods transfer class-discriminative knowledge from a fully-labeled source dataset to a scarcely-labeled target dataset. However, conventional me...Show More
Few-shot unsupervised domain adaptation for SAR target recognition based on input alignment and class confusion loss
2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP)
Year: 2024 | Conference Paper |
With the development of simulation technologies and deep learning, utilizing simulated images to train Synthetic Aperture Radar (SAR) target recognition model has become a research hotspot. However, SAR target recognition is hindered by the domain gap between simulated and real images, particularly under few-shot settings which mean only a limited number of unlabeled target domain samples are avai...Show More
Underwater Target Recognition Method Based on Domain Adaptation
2021 4th International Conference on Pattern Recognition and Artificial Intelligence (PRAI)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
In this paper, a deep multi-core metric domain adaptation algorithm suitable for underwater target recognition, which introduces divergence deviation metric and multi-core technology, is proposed. In particular, the maximum mean difference is used for the metric distribution distance difference, and only the first-order statistics of the data distribution is considered. When the data distribution ...Show More
An Open Set Domain Adaptation Network Based on Adversarial Learning for Remote Sensing Image Scene Classification
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
Year: 2020 | Conference Paper |
Cited by: Papers (13)
Remote sensing image scene classification refers to assigning specific semantic labels for remote sensing images. Due to the lack of labeled remote sensing images, domain adaptation is applied to remote sensing image scene classification. However, recent proposed methods mainly focus on the closed set scenario. In this paper, we explore the open set scenario and introduce an open set domain adapta...Show More
Class Ambiguity Regularized Adversarial Domain Adaptation for Cross-resolution SAR ATR
Yuchun Lu;Jifang Pei;Yi Liu;Yin Zhang;Weibo Huo;Yulin Huang;Jianyu Yang;Jianqi Wu
2024 IEEE Radar Conference (RadarConf24)
Year: 2024 | Conference Paper |
Automatic target recognition (ATR) plays a critical role in synthetic aperture radar (SAR) applications. However, existing SAR ATR methods usually assume that the training and test SAR samples have the same imaging resolution but it is hard to satisfy in practice due to the complexity of the SAR imaging process, which results in the discrepancy between different resolution domains and degrades the...Show More
Adaptive Face Recognition Using Adversarial Information Network
In many real-world applications, face recognition models often degenerate when training data (referred to as source domain) are different from testing data (referred to as target domain). To alleviate this mismatch caused by some factors like pose and skin tone, the utilization of pseudo-labels generated by clustering algorithms is an effective way in unsupervised domain adaptation. However, they ...Show More
An Exemplar-Based Multi-View Domain Generalization Framework for Visual Recognition
In this paper, we propose a new exemplar-based multi-view domain generalization (EMVDG) framework for visual recognition by learning robust classifier that are able to generalize well to arbitrary target domain based on the training samples with multiple types of features (i.e., multi-view features). In this framework, we aim to address two issues simultaneously. First, the distribution of trainin...Show More
Hallucinating Agnostic Images to Generalize Across Domains
Fabio Maria Carlucci;Paolo Russo;Tatiana Tommasi;Barbara Caputo
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
Year: 2019 | Conference Paper |
Cited by: Papers (22)
The ability to generalize across visual domains is crucial for the robustness of artificial recognition systems. Although many training sources may be available in real contexts, the access to even unlabeled target samples cannot be taken for granted, which makes standard unsupervised domain adaptation methods inapplicable in the wild. In this work we investigate how to exploit multiple sources by...Show More
MCKD: Mutually Collaborative Knowledge Distillation For Federated Domain Adaptation And Generalization
Ziwei Niu;Hongyi Wang;Hao Sun;Shuyi Ouyang;Yen-wei Chen;Lanfen Lin
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Conventional unsupervised domain adaptation (UDA) and domain generalization (DG) methods rely on the assumption that all source domains can be directly accessed and combined for model training. However, this centralized training strategy may violate privacy policies in many real-world applications. A paradigm for tackling this problem is to train multiple local models and aggregate a generalized c...Show More
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends t...Show More
Progressive Mixup Augmented Teacher-Student Learning for Unsupervised Domain Adaptation
Aotian Zheng;Jie Mei;Farron Wallace;Craig Rose;Rania Hussein;Jenq-Neng Hwang
2023 IEEE International Conference on Image Processing (ICIP)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Unsupervised Domain Adaptation (UDA) aims to transfer knowledge learned from a labeled source domain to an unlabeled target domain, mostly through learning a domain invariant feature representation. Currently, the best performing UDA methods use category level domain alignment to capture fine-grained information, resulting in significantly improved performance over global alignment. While successf...Show More
An Unsupervised Domain Adaption Framework for Aerial Image Semantic Segmentation Based on Curriculum Learning
Lingyan Ran;Cheng Ji;Shizhou Zhang;Xiaoqiang Zhang;Yanning Zhang
2022 7th International Conference on Image, Vision and Computing (ICIVC)
Year: 2022 | Conference Paper |
Cited by: Papers (3)
With the development of deep learning, semantic segmentation has made breakthrough progress, but supervised learning requires a large amount of data with pixel-level annotation. However, for remote sensing data, it is difficult to obtain large-scale pixel-level datasets. There is visual differences between the data of different geospatial regions inevitably. In particular, this difference is often...Show More
Dual-Consistency Self-Training For Unsupervised Domain Adaptation
Jie Wang;Chaoliang Zhong;Cheng Feng;Jun Sun;Masaru Ide;Yasuto Yokota
2021 IEEE International Conference on Image Processing (ICIP)
Year: 2021 | Conference Paper |
Unsupervised domain adaptation (UDA) is a challenging task characterized by unlabeled target data with domain discrepancy to labeled source data. Many methods have been proposed to learn domain invariant features by marginal distribution alignment, but they ignore the intrinsic structure within target domain, which may lead to insufficient or false alignment. Class-level alignment has been demonst...Show More
Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
Behnam Gholami;Pritish Sahu;Ognjen Rudovic;Konstantinos Bousmalis;Vladimir Pavlovic
Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where there is a single, labeled, source and a single target domain. However, in many real-world settings one seeks to adapt to multiple, but somewhat similar, target domains. Applying pairwise adaptation approaches to this setting may be suboptimal, as they fail to leverage shared information among multiple domains....Show More
Distributed Heterogeneous Transfer Learning for Link Prediction in the Positive Unlabeled Setting
2022 IEEE International Conference on Big Data (Big Data)
Year: 2022 | Conference Paper |
Cited by: Papers (8)
Transfer learning focuses on enhancing predictive models for a target domain, by exploiting the knowledge coming from a related source domain. However, most existing transfer learning methods assume that source and target domains are described with the same feature spaces. Heterogeneous transfer learning approaches aim to overcome this limitation, but they usually introduce strong assumptions (e.g...Show More
Unsupervised Domain Adaptation for Brain Vessel Segmentation Through Transwarp Contrastive Learning
Fengming Lin;Yan Xia;Yash Deo;Michael MacRaild;Haoran Dou;Qiongyao Liu;Kun Wu;Nishant Ravikumar;Alejandro F. Frangi
2024 IEEE International Symposium on Biomedical Imaging (ISBI)
Year: 2024 | Conference Paper |
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. Since cross-modality medical data exhibit significant intra and interdomain shifts and most are unlabelled, UDA is more important while challenging in medical image analysis. This paper proposes a simple yet potent contrastive lea...Show More
Attention-Based Multi-Source Domain Adaptation
Multi-source domain adaptation (MSDA) aims to transfer knowledge from multi-source domains to one target domain. Inspired by single-source domain adaptation, existing methods solve MSDA by aligning the data distributions between the target domain and each source domain. However, aligning the target domain with the dissimilar source domain would harm the representation learning. To address the abov...Show More
WiSDA: Subdomain Adaptation Human Activity Recognition Method Using Wi-Fi Signals
Human activity recognition based on Wi-Fi signals has become one part of integrated sensing and communications, which has promising application prospects. Detecting activities across different domains is an important and challenging problem. To reduce model complexity and improve recognition accuracy, we propose a novel approach to realize activity recognition across domains, named WiSDA. The prop...Show More
Deep Clustering for Domain Adaptation
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
We address the heterogeneous domain adaptation task: adapting a classifier trained on data from one domain to operate on another domain that also has a different label space. We consider two settings that both exhibit label scarcity of some form—one where only unlabelled data is available, and another where a small volume of labelled data is available in addition to the unlabelled data. Our method...Show More
Domain Adaptation for SAR Target Recognition with Limited Training Data Via Rigid Transformation-Based Feature Conversion
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Year: 2022 | Conference Paper |
Cited by: Papers (3)
This paper presents a semi-supervised domain adaptation method for SAR target recognition. The proposed method only requires a few real data to be labeled. The challenge is that due to the high angle sensitivity of SAR images, a network can easily overfit the training data at seen angles and fails to classify data at unseen angles. To overcome this, we design a conversion module that can infer wha...Show More
IFDS: An Intelligent Fault Diagnosis System With Multisource Unsupervised Domain Adaptation for Different Working Conditions
Intelligent fault diagnosis is an important subject of mechanical system maintenance. Domain adaptation is a method to solve the problem that the model trained on the training set (source domain) is not suitable for the test set (target domain) due to different working conditions in fault diagnosis. In industrial scenarios, there may be multiple-source domains. For this reason, we proposed an inte...Show More
Hierarchical semisupervised transfer AdaBoost
2014 IEEE 7th Joint International Information Technology and Artificial Intelligence Conference
Year: 2014 | Conference Paper |
We propose a hierarchical and semisupervised transfer AdaBoost (HissTrAdaBoost) algorithm to address over-fitting and generalization problem in TrAdaBoost, which is one of the state-of-the-art instance based transfer learning algorithm. Specifically, the samples in the source domain which have larger difference from the target domain are removed, and then the unlabeled instances in the target doma...Show More
Transfer Learning with Shared and Specific Structures for SAR Target Recognition
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
Year: 2022 | Conference Paper |
Cited by: Papers (1)
While most existing transfer learning methods map the source domain and the target domain data into a common space by sharing the model, the specific knowledge with private properties may be lost. To address this problem, we propose a transfer learning model with shared and specific structures for synthetic aperture radar (SAR) target recognition in this paper. Firstly, we design a convolutional n...Show More
Deep Targeted Transfer Learning Along Designable Adaptation Trajectory for Fault Diagnosis Across Different Machines
Deep transfer learning-based fault diagnosis has been developed to correct the data distribution shift, promoting a diagnosis knowledge transfer across related machines. However, there are two weaknesses: first, the assumption that all the target domain data are unlabeled is strict for robust applications of deep transfer learning to diagnosis across different machines; and second, the successes o...Show More
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