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Unsupervised Domain Adaptation Using Exemplar-SVMs with Adaptation Regularization
Complexity, 2018 Domain adaptation has recently attracted attention for visual recognition. It assumes that source and target domain data are drawn from the same feature space but different margin distributions and its motivation is to utilize the source domain instances
Yiwei He +3 more
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Feature-Level Domain Adaptation [PDF]
, 2015 Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, whilst test data is sampled from a target domain. This paper proposes and studies an approach, called feature-level domain adaptation (FLDA), that models the dependence between
Kouw, Wouter M. +3 more
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Fluctuation domains in adaptive evolution [PDF]
Theoretical Population Biology, 2010 We derive an expression for the variation between parallel trajectories in phenotypic evolution, extending the well known result that predicts the mean evolutionary path in adaptive dynamics or quantitative genetics. We show how this expression gives rise to the notion of fluctuation domains - parts of the fitness landscape where the rate of evolution ...
Boettiger, Carl +2 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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A framework for self-supervised federated domain adaptation
EURASIP Journal on Wireless Communications and Networking, 2022 Unsupervised federated domain adaptation uses the knowledge from several distributed unlabelled source domains to complete the learning on the unlabelled target domain.
Bin Wang +5 more
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Unsupervised Domain Adaptation with Adapter
, 2021 Unsupervised domain adaptation (UDA) with pre-trained language models (PrLM) has achieved promising results since these pre-trained models embed generic knowledge learned from various domains. However, fine-tuning all the parameters of the PrLM on a small domain-specific corpus distort the learned generic knowledge, and it is also expensive to ...
Zhang, Rongsheng +3 more
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Self-Adaptive Partial Domain Adaptation
, 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 ...
Hu, Jian +8 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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Multi-Adversarial Domain Adaptation
, 2018 Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains.
Cao, Zhangjie +3 more
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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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