Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color i...
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Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection mo...
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Medical image segmentation (i.e., liver segmentation) is an important pre-processing for computer-aided diagnosis and computer-aided surgery. Deep learning, which can perform fully automated segmentation, has been pop...
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Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to disentangle domain-specific and domain...
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With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve...
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Chest radiography is widely used in annual medical screening to check whether lungs are healthy or not. Therefore it would be desirable to develop an intelligent system to help clinicians automatically detect potentia...
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Machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlationa...
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The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic ...
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Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing explainable recommendations only utilize ...
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Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise *...
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Recent progress in material data mining has been driven by high-capacity models trained on large ***,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise ***,we develop a novel transfer learning strategy to address problems of small or insufficient *** strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining *** a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real *** results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real *** the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.
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