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• W2770601096 abstract "We propose a new deep network structure for unconstrained face recognition. The proposed network integrates several key components together in order to characterize complex data distributions, such as in unconstrained face images. Inspired by recent progress in deep networks, we consider some important concepts, including multi-scale feature learning, dense connections of network layers, and weighting different network flows, for building our deep network structure. The developed network is evaluated in unconstrained face matching, showing the capability of learning complex data distributions caused by face images with various qualities." @default.
• W2770601096 created "2017-12-04" @default.
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• W2770601096 date "2017-11-27" @default.
• W2770601096 modified "2023-09-26" @default.
• W2770601096 title "Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition" @default.
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• W2770601096 doi "https://doi.org/10.48550/arxiv.1711.10103" @default.
• W2770601096 hasPublicationYear "2017" @default.
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