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Generation, augmentation, and alignment: a pseudo-source domain based method for source-free domain adaptation
Source-free domain adaptation (SFDA) aims to train a well-performed model in the target domain given both a trained source model and unlabeled target...
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Source bias reduction for source-free domain adaptation
Source-free domain adaptation (SFDA) mainly aims to the problem of not being able to access the source domain data during the model migration...
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Multi-source-free Domain Adaptive Object Detection
To enhance the transferability of object detection models in real-world scenarios where data is sampled from disparate distributions, considerable...
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Crots: Cross-Domain Teacher–Student Learning for Source-Free Domain Adaptive Semantic Segmentation
Source-free domain adaptation (SFDA) aims to transfer source knowledge to the target domain from pre-trained source models without accessing private...
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Domain-specific feature elimination: multi-source domain adaptation for image classification
Multi-source domain adaptation utilizes multiple source domains to learn the knowledge and transfers it to an unlabeled target domain. To address the...
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Rethinking confidence scores for source-free unsupervised domain adaptation
Source-free unsupervised domain adaptation (SFUDA) aims to achieve target domain predictions through a source model instead of source data. One of...
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Dual collaboration for decentralized multi-source domain adaptation
The goal of decentralized multi-source domain adaptation is to conduct unsupervised multi-source domain adaptation in a data decentralization...
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Source-Free Domain Adaptation via Target Prediction Distribution Searching
Existing Source-Free Domain Adaptation (SFDA) methods typically adopt the feature distribution alignment paradigm via mining auxiliary information...
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Two-stage structural information enhancement for source-free domain adaptation
Source-free domain adaptation (SFDA) uses models trained from source domains to solve similar tasks in unlabeled domains, without accessing source...
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Cross Domain Pulmonary Nodule Detection Without Source Data
The model performance on cross-domain pulmonary nodule detection usually degrades because of the significant shift in data distributions and the...
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Multi-source domain generalization peron re-identification with knowledge accumulation and distribution enhancement
Domain generalization person re-identification (re-ID) is a more realistic task that aims to learn a model with multiple labeled source domains and...
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Graph-based fine-grained model selection for multi-source domain
The prosperity of datasets and model architectures has led to the development of pretrained source models, which simplified the learning process in...
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Source-Free Unsupervised Domain Adaptation
This chapter discusses SFDA, where models trained on labeled source data need to adapt to unlabeled target data without accessing the original source...
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Continual Source-Free Unsupervised Domain Adaptation
Source-free Unsupervised Domain Adaptation (SUDA) approaches inherently exhibit catastrophic forgetting. Typically, models trained on a labeled...
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Self-training transformer for source-free domain adaptation
In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation. Previous...
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Domain adaptation based on source category prototypes
Unsupervised domain adaptation (UDA), which can transfer knowledge from labeled source domain to unlabeled target domain, needs to access a large...
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Domain adversarial-based multi-source deep transfer network for cross-production-line time series forecasting
In industrial settings, building a time series prediction model for new production lines or equipment with new sensors can be challenging due to a...
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Weighted progressive alignment for multi-source domain adaptation
Multi-source domain adaptation (MSDA) dedicates to establishing knowledge transfer from multiple labeled source domains to an unlabeled target...
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Multi-modal Component Representation for Multi-source Domain Adaptation Method
Multi-source domain adaptation aims to leverage multiple labeled source domains to train a classifier for an unlabeled target domain. Existing...
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Development of a speech separation system using frequency domain blind source separation technique
Professionals can interact while communicating remotely with teleconferencing. It enables communication between users using computers, smartphones,...
