Registro de evidencia del método

VGGNet

VGGNet is a deep convolutional neural network architecture introduced by Karen Simonyan and Andrew Zisserman at the Visual Geometry Group, Oxford, in 2014 (published at ICLR 2015). It demonstrated that network depth — achieved exclusively through stacking small 3x3 convolutional filters — is the single most critical factor for high image-classification accuracy, and its two canonical variants (VGG-16 and VGG-19) became the dominant benchmark architectures for CNN design throughout the mid-2010s.

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Very Deep Convolutional Networks for Large-Scale Image Recognition (VGGNet)

Registro del método taxonómico · ml-model / deep-learning

Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556 [cs.CV]. Published at ICLR 2015. · DOI 10.48550/arXiv.1409.1556
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning (Ch. 9: Convolutional Networks). MIT Press. · ISBN 978-0-262-03561-3

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Afirmaciones curadas

Afirmaciones persistidas en el libro mayor de evidencia, cada una con su propia evaluación.

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Métodos relacionados

Generado a partir del grafo de métodos y mostrado como relaciones sugeridas por la máquina; no se infiere ninguna afirmación de evidencia.

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