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Unsupervised Domain Adaptation Recent Advances and Future Perspectives
Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and...
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Low Rank Adaptation for Stable Domain Adaptation of Vision Transformers
AbstractUnsupervised domain adaptation plays a crucial role in semantic segmentation tasks due to the high cost of annotating data. Existing...
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Introduction to Domain Adaptation
Domain adaptation refers to the machine learning techniques that enable models trained on data from a source domain to perform well on a different...
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Moka-ADA: adversarial domain adaptation with model-oriented knowledge adaptation for cross-domain sentiment analysis
Cross-domain sentiment analysis (CDSA) aims to overcome domain discrepancy to judge the sentiment polarity of the target domain lacking labeled data....
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Unsupervised Domain Adaptation Techniques
This chapter provides an overview of unsupervised domain adaptation techniques. First, we identify key challenges and limitations in current...
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Subdomain adaptation via correlation alignment with entropy minimization for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) is a well-explored domain in transfer learning, finding applications across various real-world scenarios. The...
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Domain consensual contrastive learning for few-shot universal domain adaptation
Traditional unsupervised domain adaptation (UDA) aims to transfer the learned knowledge from a fully labeled source domain to another unlabeled...
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Class-conditional domain adaptation for semantic segmentation
Semantic segmentation is an important sub-task for many applications. However, pixel-level ground-truth labeling is costly, and there is a tendency...
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Unsupervised Domain Adaptation for Cross-domain Histopathology Image Classification
Unsupervised domain adaptation (UDA) methods have made remarkable progress in histopathological image analysis and various cancer diagnosis domains....
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Domain-Agnostic Priors for Semantic Segmentation Under Unsupervised Domain Adaptation and Domain Generalization
In computer vision, an important challenge to deep neural networks comes from adjusting the varying properties of different image domains. To study...
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FMDADA: Federated multi-discriminative adversarial domain adaptation
Federated domain adaptation system aims to address the problem of domain shift in a federated learning (FL) framework, where knowledge learned from...
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Domain Adaptation for Learning from Label Proportions Using Domain-Adversarial Neural Network
Learning from Label Proportions (LLP) is a machine learning problem where the training data are composed of bags of instances, and only the class...
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NaCL: noise-robust cross-domain contrastive learning for unsupervised domain adaptation
The Unsupervised Domain Adaptation (UDA) methods aim to enhance feature transferability possibly at the expense of feature discriminability....
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Multi-scale iterative domain adaptation for specific emitter identification
Specific emitter identification (SEI) is a technology that identifies different emitters through their unique characteristics. Research on...
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Towards Explainable Deep Domain Adaptation
In many practical applications data used for training a machine learning model and the deployment data does not always preserve the same...
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Unsupervised domain adaptation for object detection through mixed-domain and co-training learning
As the data distribution difference between the target domain (test sample set) and the source domain (training sample set) increases, it may lead to...
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Maximizing conditional independence for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) studies how to transfer a learner from a labeled source domain to an unlabeled target domain with different...
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Dynamic parameterized learning for unsupervised domain adaptation
Unsupervised domain adaptation enables neural networks to transfer from a labeled source domain to an unlabeled target domain by learning...
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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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Unsupervised Domain Adaptation on Sentence Matching Through Self-Supervision
Although neural approaches have yielded state-of-the-art results in the sentence matching task, their performance inevitably drops dramatically when...