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- W4298861830 abstract "This paper proposes a generative ScatterNet hybrid deep learning (G-SHDL) network for semantic image segmentation. The proposed generative architecture is able to train rapidly from relatively small labeled datasets using the introduced structural priors. In addition, the number of filters in each layer of the architecture is optimized resulting in a computationally efficient architecture. The G-SHDL network produces state-of-the-art classification performance against unsupervised and semi-supervised learning on two image datasets. Advantages of the G-SHDL network over supervised methods are demonstrated with experiments performed on training datasets of reduced size." @default.
- W4298861830 created "2022-10-02" @default.
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- W4298861830 date "2018-02-09" @default.
- W4298861830 modified "2023-09-27" @default.
- W4298861830 title "Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural Priors for Semantic Image Segmentation" @default.
- W4298861830 doi "https://doi.org/10.48550/arxiv.1802.03374" @default.
- W4298861830 hasPublicationYear "2018" @default.
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