Results 31 to 40 of about 482,653 (299)
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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Transfer bounds for linear feature learning [PDF]
Machine Learning, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Attention-based Wav2Text with feature transfer learning [PDF]
2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2017 Conventional automatic speech recognition (ASR) typically performs multi-level pattern recognition tasks that map the acoustic speech waveform into a hierarchy of speech units. But, it is widely known that information loss in the earlier stage can propagate through the later stages.
Andros Tjandra +2 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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Using Decoupled Features for Photorealistic Style Transfer
SIAM Journal on Imaging Sciences, 2023 In this work we propose a photorealistic style transfer method for image and video that is based on vision science principles and on a recent mathematical formulation for the deterministic decoupling of sample statistics. The novel aspects of our approach include matching decoupled moments of higher order than in common style transfer approaches, and ...
Trevor D. Canham +3 more
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Abstract Feature Space Representation for Volumetric Transfer Function Exploration [PDF]
, 2011 The application of n-dimensional transfer functions for feature segmentation has become increasingly popular in volume rendering. Recent work has focused on the utilization of higher order dimensional transfer functions incorporating spatial dimensions ...
Ebert, David S. +3 more
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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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Exploring Diamond-Like Lattice Thermal Conductivity Crystals via Feature-Based Transfer Learning
, 2019 Ultrahigh lattice thermal conductivity materials hold great importance since they play a critical role in the thermal management of electronic and optical devices.
Shenghong, Ju +5 more
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Unsupervised Local Feature Hashing for Image Similarity Search
, 2016 The potential value of hashing techniques has led to it becoming one of the most active research areas in computer vision and multimedia. However, most existing hashing methods for image search and retrieval are based on global feature representations ...
Liu, Li, Shao, Ling, Yu, Mengyang
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