Results 31 to 40 of about 389,599 (264)
Associative Domain Adaptation [PDF]
2017 IEEE International Conference on Computer Vision (ICCV), 2017 We propose associative domain adaptation, a novel technique for end-to-end domain adaptation with neural networks, the task of inferring class labels for an unlabeled target domain based on the statistical properties of a labeled source domain. Our training scheme follows the paradigm that in order to effectively derive class labels for the target ...
Philip Häusser +3 more
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Self-Adaptive Partial Domain Adaptation
CoRR, 2021 Partial Domain adaptation (PDA) aims to solve a more practical cross-domain learning problem that assumes target label space is a subset of source label space. However, the mismatched label space causes significant negative transfer. A traditional solution is using soft weights to increase weights of source shared domain and reduce those of source ...
Jian Hu 0002 +8 more
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Background-Aware Domain Adaptation for Plant Counting
Frontiers in Plant Science, 2022 Deep learning-based object counting models have recently been considered preferable choices for plant counting. However, the performance of these data-driven methods would probably deteriorate when a discrepancy exists between the training and testing ...
Min Shi, Xing-Yi Li, Hao Lu, Zhi-Guo Cao
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CoRR, 2020 Unsupervised domain adaptation (UDA) is widely used to transfer knowledge from a labeled source domain to an unlabeled target domain with different data distribution. While extensive studies attested that deep learning models are vulnerable to adversarial attacks, the adversarial robustness of models in domain adaptation application has largely been ...
Jiajin Zhang, Hanqing Chao, Pingkun Yan
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Reciprocal normalization for domain adaptation
Pattern Recognition, 2023 Batch normalization (BN) is widely used in modern deep neural networks, which has been shown to represent the domain-related knowledge, and thus is ineffective for cross-domain tasks like unsupervised domain adaptation (UDA). Existing BN variant methods aggregate source and target domain knowledge in the same channel in normalization module.
Zhiyong Huang 0009 +7 more
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Kernel Manifold Alignment for Domain Adaptation. [PDF]
PLoS ONE, 2016 The wealth of sensory data coming from different modalities has opened numerous opportunities for data analysis. The data are of increasing volume, complexity and dimensionality, thus calling for new methodological innovations towards multimodal data ...
Devis Tuia, Gustau Camps-Valls
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Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder
Applied Sciences, 2022 Unsupervised domain adaptation involves knowledge transfer from a labeled source to unlabeled target domains to assist target learning tasks. A critical aspect of unsupervised domain adaptation is the learning of more transferable and distinct feature ...
Yi Zhu, Xinke Zhou, Xindong Wu
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Discriminativeness-Preserved Domain Adaptation for Few-Shot Learning
IEEE Access, 2020 Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples.
Guangzhen Liu, Zhiwu Lu
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Bilateral co-transfer for unsupervised domain adaptation
Journal of Automation and Intelligence, 2023 Labeled data scarcity of an interested domain is often a serious problem in machine learning. Leveraging the labeled data from other semantic-related yet co-variate shifted source domain to facilitate the interested domain is a consensus.
Fuxiang Huang, Jingru Fu, Lei Zhang
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Domain Adaptation in Regression [PDF]
, 2011 This paper presents a series of new results for domain adaptation in the regression setting. We prove that the discrepancy is a distance for the squared loss when the hypothesis set is the reproducing kernel Hilbert space induced by a universal kernel such as the Gaussian kernel.
Corinna Cortes, Mehryar Mohri
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