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- W3027528060 abstract "Art-style recognition is a popular problem among both the art appreciators and machine-learning scientist. In this paper, we applied convolutional neural network and transfer learning for image-style recognition. Among the three pre-trained model, ResNet-50 provided with the best performance than NASNet Mobile and VGG19. ResNet-50 brought about 71% accuracy on Kaggle dataset and 72.58% accuracy on Pandora dataset determining three categories—Expressionism, Impressionism and Surrealism. The main idea is that we tried to achieve better performance than the existing ones using transfer learning on small dataset without any sophisticated feature engineering and it is comparable to the obtained results in the literature." @default.
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- W3027528060 date "2020-01-01" @default.
- W3027528060 modified "2023-10-02" @default.
- W3027528060 title "A Closer Look into Paintings’ Style Using Convolutional Neural Network with Transfer Learning" @default.
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- W3027528060 doi "https://doi.org/10.1007/978-981-15-3607-6_26" @default.
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