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IEEE Transactions on Neural Networks and Learning Systems, 2023
Domain adaptation is concerned with the problem of generalizing a classification model to a target domain with little or no labeled data, by leveraging the abundant labeled data from a related source domain. The source and target domains possess different joint probability distributions, making it challenging for model generalization.
Sentao Chen +3 more
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Domain adaptation is concerned with the problem of generalizing a classification model to a target domain with little or no labeled data, by leveraging the abundant labeled data from a related source domain. The source and target domains possess different joint probability distributions, making it challenging for model generalization.
Sentao Chen +3 more
openaire +2 more sources
Adaptive Component Embedding for Domain Adaptation
IEEE Transactions on Cybernetics, 2021Domain adaptation is suitable for transferring knowledge learned from one domain to a different but related domain. Considering the substantially large domain discrepancies, learning a more generalized feature representation is crucial for domain adaptation.
Mengmeng Jing +5 more
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Interpretable bilinear attention network with domain adaptation improves drug–target prediction
Nature Machine Intelligence, 2022Predicting drug–target interaction is key for drug discovery. Recent deep learning-based methods show promising performance, but two challenges remain: how to explicitly model and learn local interactions between drugs and targets for better prediction ...
Peizhen Bai +3 more
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Domain Adaptation Multitask Optimization
IEEE Transactions on Cybernetics, 2023Multitask optimization (MTO) is a new optimization paradigm that leverages useful information contained in multiple tasks to help solve each other. It attracts increasing attention in recent years and gains significant performance improvements. However, the solutions of distinct tasks usually obey different distributions.
Xiaoling Wang +4 more
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Deep CORAL: Correlation Alignment for Deep Domain Adaptation
ECCV Workshops, 2016Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input distributions.
Baochen Sun, Kate Saenko
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DACH: Domain Adaptation Without Domain Information
IEEE Transactions on Neural Networks and Learning Systems, 2020Domain adaptation is becoming increasingly important for learning systems in recent years, especially with the growing diversification of data domains in real-world applications, such as the genetic data from various sequencing platforms and video feeds from multiple surveillance cameras.
Ruichu Cai +4 more
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Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation
ACM Transactions on Multimedia Computing, Communications, and Applications, 2022Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain.
Jinfeng Li +5 more
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ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Computer Vision and Pattern Recognition, 2018Semantic segmentation is a key problem for many computer vision tasks. While approaches based on convolutional neural networks constantly break new records on different benchmarks, generalizing well to diverse testing environments remains a major ...
Tuan-Hung Vu +4 more
semanticscholar +1 more source
Prompt-based Distribution Alignment for Unsupervised Domain Adaptation
AAAI Conference on Artificial Intelligence, 2023Recently, despite the unprecedented success of large pre-trained visual-language models (VLMs) on a wide range of downstream tasks, the real-world unsupervised domain adaptation (UDA) problem is still not well explored. Therefore, in this paper, we first
Shuanghao Bai +6 more
semanticscholar +1 more source