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- W2912543964 abstract "In order to solve the problem of limited training samples and reloading a large number of parameters in an embedded system or a chip, a hybrid model in transfer learning based on pre-trained model and top convolution layer with a scale factor updating is proposed. The pre-trained model is used to extract detail features, and the top layer network is used to fine-tune with specific tasks. By selecting a proper pre-trained data source and using data augmentation, the classification after transfer learning can be effectively carried out. Good experimental results have been achieved on CIFAR-10, CIFAR-100 and SVHN. Compared with traditional ones (single-task learning), best test accuracy can be increased by 0.21%, 0.38% and 0.23% respectively. Taking VGG-7 as an example, the convolution layer parameters can be saved by more than 77%. The results show that the top-layer features are more local and specific, while the bottom-layer features are more global and universal. Our model is with less resource consumption on the premise of high test accuracy." @default.
- W2912543964 created "2019-02-21" @default.
- W2912543964 creator A5017541431 @default.
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- W2912543964 date "2018-06-01" @default.
- W2912543964 modified "2023-09-23" @default.
- W2912543964 title "A hybrid model in transfer learning based pre-trained model and a scale factor updating" @default.
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- W2912543964 doi "https://doi.org/10.1109/iciev.2018.8641040" @default.
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