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Machine learning

Capsule Network

A Capsule Network (CapsNet) ialah seni bina pembelajaran mendalam yang diperkenalkan oleh Sara Sabour, Nicholas Frosst dan Geoffrey Hinton pada tahun 2017 yang menyusun neuron sebagai vektor (kapsul) berbanding pengaktifan skalar, supaya hierarki spatial dan maklumat pose (orientasi) dikodkan secara langsung. Ia dicadangkan untuk mengatasi kerapuhan rangkaian konvolusional terhadap perubahan sudut pandangan.

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Sumber

  1. Sabour, S., Frosst, N. & Hinton, G. E. (2017). Dynamic Routing Between Capsules. Advances in Neural Information Processing Systems (NeurIPS). link
  2. Hinton, G. E., Sabour, S. & Frosst, N. (2018). Matrix Capsules with EM Routing. International Conference on Learning Representations (ICLR). link

Cara memetik halaman ini

ScholarGate. (2026, June 1). Capsule Network (CapsNet). ScholarGate. https://scholargate.app/ms/deep-learning/capsule-network

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Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

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Dirujuk oleh

ScholarGateCapsule Network (Capsule Network (CapsNet)). Dicapai 2026-06-15 daripada https://scholargate.app/ms/deep-learning/capsule-network · Set data: https://doi.org/10.5281/zenodo.20539026