Results 31 to 40 of about 2,076,683 (280)
Feature-Based Transfer Learning Based on Distribution Similarity
IEEE Access, 2018 Transfer learning has been found helpful at enhancing the target domain's learning process by transferring useful knowledge from other different but related source domains.
Xiaofeng Zhong +5 more
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Scientific Reports, 2021 As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. On the other hand, recent advances in deep learning and transfer learning have shown significant potential in the ...
Yucheng Zhang +5 more
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Remote Sensing, 2016 In order to deal with scenarios where the training data, used to deduce a model, and the validation data have different statistical distributions, we study the problem of transformed subspace feature transfer for domain adaptation (DA) in the context of ...
Alim Samat +4 more
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Effective Transfer Learning with Label-Based Discriminative Feature Learning
Sensors, 2022 The performance of natural language processing with a transfer learning methodology has improved by applying pre-training language models to downstream tasks with a large number of general data.
Gyunyeop Kim, Sangwoo Kang
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Constrained Deep Transfer Feature Learning and its Applications
, 2017 Feature learning with deep models has achieved impressive results for both data representation and classification for various vision tasks. Deep feature learning, however, typically requires a large amount of training data, which may not be feasible for ...
Ji, Qiang, Wu, Yue
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Frontiers in Human Neuroscience, 2022 Transfer learning can improve the robustness of deep learning in the case of small samples. However, when the semantic difference between the source domain data and the target domain data is large, transfer learning easily introduces redundant features ...
Yehang Chen, Yehang Chen, Xiangmeng Chen
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, 2005 This paper summarizes the evidence of the ultraviolet properties of dust grains found in starburst galaxies. Observations of starburst galaxies clearly show that the 2175 A feature is weak or absent.
Gordon, Karl D.
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Feature Selection for Transfer Learning [PDF]
, 2011 Common assumption in most machine learning algorithms is that, labeled (source) data and unlabeled (target) data are sampled from the same distribution. However, many real world tasks violate this assumption: in temporal domains, feature distributions may vary over time, clinical studies may have sampling bias, or sometimes sufficient labeled data for ...
Selen Uguroglu, Jaime Carbonell
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Deep transfer network of heterogeneous domain feature in machine translation
High-Confidence Computing, 2022 In order to address the shortcoming of feature representation limitation in machine translation(MT) system, this paper presents a feature transfer method in MT.
Yupeng Liu, Yanan Zhang, Xiaochen Zhang
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Demystifying Neural Style Transfer [PDF]
, 2017 Neural Style Transfer has recently demonstrated very exciting results which catches eyes in both academia and industry. Despite the amazing results, the principle of neural style transfer, especially why the Gram matrices could represent style remains ...
Hou, Xiaodi +3 more
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