Translations:Convolutional Neural Networks/22/en - Revision history

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	\| '''LeNet-5''' \|\| 1998 \|\| Pioneered CNNs for handwritten digit recognition (MNIST) \|\| 5 layers		\| '''LeNet-5''' \|\| 1998 \|\| Pioneered CNNs for handwritten digit recognition (MNIST) \|\| 5 layers
	\|-		\|-
	\| '''AlexNet''' \|\| 2012 \|\| Won ImageNet; popularised ReLU, dropout, GPU training \|\| 8 layers		\| '''AlexNet''' \|\| 2012 \|\| Won ImageNet; popularised ReLU, {{Term\|dropout}}, GPU training \|\| 8 layers
	\|-		\|-
	\| '''VGGNet''' \|\| 2014 \|\| Showed depth matters; used only <math>3 \times 3</math> filters throughout \|\| 16–19 layers		\| '''VGGNet''' \|\| 2014 \|\| Showed depth matters; used only <math>3 \times 3</math> filters throughout \|\| 16–19 layers

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2026-04-27T21:57:51Z

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← Older revision		Revision as of 21:57, 27 April 2026
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	\| '''LeNet-5''' \|\| 1998 \|\| Pioneered CNNs for handwritten digit recognition (MNIST) \|\| 5 layers		\| '''LeNet-5''' \|\| 1998 \|\| Pioneered CNNs for handwritten digit recognition (MNIST) \|\| 5 layers
	\|-		\|-
	\| '''AlexNet''' \|\| 2012 \|\| Won ImageNet; popularised ReLU, ~~{{Term\|~~dropout}}, GPU training \|\| 8 layers		\| '''AlexNet''' \|\| 2012 \|\| Won ImageNet; popularised ReLU, dropout, GPU training \|\| 8 layers
	\|-		\|-
	\| '''VGGNet''' \|\| 2014 \|\| Showed depth matters; used only <math>3 \times 3</math> filters throughout \|\| 16–19 layers		\| '''VGGNet''' \|\| 2014 \|\| Showed depth matters; used only <math>3 \times 3</math> filters throughout \|\| 16–19 layers

FuzzyBot: Importing a new version from external source

2026-04-27T19:41:44Z

Importing a new version from external source

← Older revision		Revision as of 19:41, 27 April 2026
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	\| '''LeNet-5''' \|\| 1998 \|\| Pioneered CNNs for handwritten digit recognition (MNIST) \|\| 5 layers		\| '''LeNet-5''' \|\| 1998 \|\| Pioneered CNNs for handwritten digit recognition (MNIST) \|\| 5 layers
	\|-		\|-
	\| '''AlexNet''' \|\| 2012 \|\| Won ImageNet; popularised ReLU, dropout, GPU training \|\| 8 layers		\| '''AlexNet''' \|\| 2012 \|\| Won ImageNet; popularised ReLU, {{Term\|dropout}}, GPU training \|\| 8 layers
	\|-		\|-
	\| '''VGGNet''' \|\| 2014 \|\| Showed depth matters; used only <math>3 \times 3</math> filters throughout \|\| 16–19 layers		\| '''VGGNet''' \|\| 2014 \|\| Showed depth matters; used only <math>3 \times 3</math> filters throughout \|\| 16–19 layers

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2026-04-27T00:30:26Z

Importing a new version from external source

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{| class="wikitable"
|-
! Architecture !! Year !! Key contribution !! Depth
|-
| '''LeNet-5''' || 1998 || Pioneered CNNs for handwritten digit recognition (MNIST) || 5 layers
|-
| '''AlexNet''' || 2012 || Won ImageNet; popularised ReLU, dropout, GPU training || 8 layers
|-
| '''VGGNet''' || 2014 || Showed depth matters; used only <math>3 \times 3</math> filters throughout || 16–19 layers
|-
| '''GoogLeNet (Inception)''' || 2014 || Introduced inception modules with parallel filter sizes || 22 layers
|-
| '''ResNet''' || 2015 || Introduced residual connections enabling very deep networks || 50–152+ layers
|-
| '''DenseNet''' || 2017 || Connected each layer to every subsequent layer via dense blocks || 121–264 layers
|-
| '''EfficientNet''' || 2019 || Compound scaling of depth, width, and resolution || Variable
|}