Translations:Convolutional Neural Networks/31/en: Difference between revisions

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    * Use pretrained models (transfer learning) when labelled data is limited.
    * Use pretrained models (transfer learning) when labelled data is limited.
    * Prefer small kernels (<math>3 \times 3</math>) stacked in depth — two <math>3 \times 3</math> layers have the same receptive field as one <math>5 \times 5</math> layer but with fewer parameters.
    * Prefer small kernels (<math>3 \times 3</math>) stacked in depth — two <math>3 \times 3</math> layers have the same receptive field as one <math>5 \times 5</math> layer but with fewer parameters.
    * Apply batch normalisation after convolution and before activation.
    * Apply {{Term|batch normalization|batch normalisation}} after convolution and before {{Term|activation function|activation}}.
    * Use data augmentation generously to reduce [[Overfitting and Regularization|overfitting]].
    * Use data augmentation generously to reduce [[Overfitting and Regularization|overfitting]].
    * Replace fully connected layers with global average pooling to reduce parameters.
    * Replace fully connected layers with global average {{Term|pooling}} to reduce parameters.

    Latest revision as of 23:34, 27 April 2026

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    Message definition (Convolutional Neural Networks)
    * Use pretrained models (transfer learning) when labelled data is limited.
    * Prefer small kernels (<math>3 \times 3</math>) stacked in depth — two <math>3 \times 3</math> layers have the same receptive field as one <math>5 \times 5</math> layer but with fewer parameters.
    * Apply {{Term|batch normalization|batch normalisation}} after convolution and before {{Term|activation function|activation}}.
    * Use data augmentation generously to reduce [[Overfitting and Regularization|overfitting]].
    * Replace fully connected layers with global average {{Term|pooling}} to reduce parameters.
    • Use pretrained models (transfer learning) when labelled data is limited.
    • Prefer small kernels ($ 3 \times 3 $) stacked in depth — two $ 3 \times 3 $ layers have the same receptive field as one $ 5 \times 5 $ layer but with fewer parameters.
    • Apply batch normalisation after convolution and before activation.
    • Use data augmentation generously to reduce overfitting.
    • Replace fully connected layers with global average pooling to reduce parameters.
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