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Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation [PDF]
, 2019 Speech emotion recognition plays an important role in building more intelligent and human-like agents. Due to the difficulty of collecting speech emotional data, an increasingly popular solution is leveraging a related and rich source corpus to help ...
Chen, Ke, Zhou, Hao
core +2 more sources
O2M-UDA: Unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation
Knowledge-Based Systems, 2023 One-to-multiple medical image segmentation aims to directly test a segmentation model trained with the medical images of a one-domain site on those of a multiple-domain site, suffering from segmentation performance degradation on multiple domains. This process avoids additional annotations and helps improve the application value of the model.
Ziyue Jiang +9 more
openaire +2 more sources
Aero-Engine Remaining Useful Life Prediction Based on Bi-Discrepancy Network
Sensors, 2023 Most unsupervised domain adaptation (UDA) methods align feature distributions across different domains through adversarial learning. However, many of them require introducing an auxiliary domain alignment model, which incurs additional computational ...
Nachuan Liu +3 more
doaj +1 more source
IEEE Access, 2023 Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally, geometry-based alignment methods, e.g., Orthogonal Procrustes Alignment (OPA), formed an important class ...
Kowshik Thopalli +3 more
doaj +1 more source
Transferable adversarial masked self-distillation for unsupervised domain adaptation
Complex & Intelligent Systems, 2023 Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to a related unlabeled target domain. Most existing works focus on minimizing the domain discrepancy to learn global domain-invariant representation using CNN ...
Yuelong Xia, Li-Jun Yun, Chengfu Yang
doaj +1 more source
Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation with Transformer
Remote Sensing, 2022 With the development of deep learning, the performance of image semantic segmentation in remote sensing has been constantly improved. However, the performance usually degrades while testing on different datasets because of the domain gap.
Weitao Li +3 more
doaj +1 more source
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis
, 2020 Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA) aims to leverage useful information in multiple source domains to help do SA in an unlabeled target domain that has no supervised information.
Dai, Yong +3 more
core +1 more source
Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation
, 2023 Domain Adaptation (DA) is always challenged by the spurious correlation between domain-invariant features (e.g., class identity) and domain-specific features (e.g., environment) that does not generalize to the target domain. Unfortunately, even enriched with additional unsupervised target domains, existing Unsupervised DA (UDA) methods still suffer ...
YUE, Zhongqi +2 more
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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 10 pages, 7 figures, CVPR ...
Shin, Hyungseob +5 more
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Applied SciencesUnsupervised domain adaptation (UDA) presents a significant challenge in sentiment analysis, especially when faced with differences between source and target domains.
Haidi Badr, Nayer Wanas, Magda Fayek
doaj +1 more source