Results 21 to 30 of about 2,079,458 (324)
Facial Expression Transfer Based on Conditional Generative Adversarial Networks
IEEE Access, 2023 With the development of computer vision and image transfer, facial expression transfer has been more and more widespread applications. But there are still some problems, such as lack of realistic expression, poor retention of facial identity features and
Yang Fan +3 more
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Knowledge Transfer Using Local Features [PDF]
2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning, 2007 We present a method for reducing the effort required to compute policies for tasks based on solutions to previously solved tasks. The key idea is to use a learned intermediate policy based on local features to create an initial policy for the new task. In order to further improve this initial policy, we developed a form of generalized policy iteration.
Martin Stolle, Christopher G. Atkeson
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Feature Spaces-based Transfer Learning [PDF]
Advances in Intelligent Systems Research, 2015 Transfer learning provides an approach to solve target tasks more quickly and effectively by using previouslyacquired knowledge learned from source tasks. Most of transfer learning approaches extract knowledge of source domain in the given feature space. The issue is that single perspective can‟t mine the relationship of source domain and target domain
Jie Lu +3 more
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Feature Map Regularized CycleGAN for Domain Transfer
Mathematics, 2023 CycleGAN domain transfer architectures use cycle consistency loss mechanisms to enforce the bijectivity of highly underconstrained domain transfer mapping.
Lidija Krstanović +3 more
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Development and Testing of a 2-D Transfer CCD [PDF]
, 2006 This paper describes the development, operation, and characterization of charge-coupled devices (CCDs) that feature an electrode structure that allows the transfer of charge both horizontally and vertically through the image area. Such devices have been
Burt, DJ +5 more
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Transfer Learning across Feature-Rich Heterogeneous Feature Spaces via Feature-Space Remapping (FSR) [PDF]
ACM Transactions on Intelligent Systems and Technology, 2015 Transfer learning aims to improve performance on a target task by utilizing previous knowledge learned from source tasks. In this paper we introduce a novel heterogeneous transfer learning technique, Feature-Space Remapping (FSR), which transfers knowledge between domains with different feature spaces.
Kyle D, Feuz, Diane J, Cook
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IEEE Access, 2020 Detecting the frequency of the pest occurrence is always a time consuming and laborious task for agriculture. This paper attempts to solve the problem through the combination of deep learning and pest detection.
Yuh-Shyan Chen +2 more
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Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images [PDF]
, 2018 Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer ...
FA Spanhol +8 more
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Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer
Sensors, 2022 For conventional near-infrared spectroscopy (NIR) technology, even within the same sample, the NIR spectral signal can vary significantly with variation of spectrometers and the spectral collection environment.
Zheyu Zhang +4 more
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On the 10-micron silicate feature in Active Galactic Nuclei [PDF]
, 2009 The 10-micron silicate feature observed with Spitzer in active galactic nuclei (AGN) reveals some puzzling behavior. It (1) has been detected in emission in type 2 sources, (2) shows broad, flat-topped emission peaks shifted toward long wavelengths in ...
Elitzur, Moshe +2 more
core +3 more sources