Translations:Convolutional Neural Networks/31/en

• 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.