LeNet - 5 made extensive use of padding to create valid convolutions, to avoid increasing the number of channels after every convolutional layer. True/False?

This statement is False.

Explanation:

LeNet-5, a pioneering convolutional neural network architecture developed by Yann LeCun et al., does indeed utilize padding in its architecture. However, the main purpose of padding in this context is to control the spatial dimensions (height and width) of the output feature maps rather than to avoid increasing the number of channels after every convolutional layer.

1. Padding: In convolutional neural networks, padding is added to the input feature maps to ensure that the spatial dimensions of the feature maps remain manageable through the layers. It helps preserve the size of the output and effectively allows the network to learn features at the edges of the input.

2. Channels: In typical CNN architectures, including LeNet-5, the number of channels (depth) actually increases after several convolutional layers to capture more complex features. In LeNet-5, the convolutional layers increase from 1 channel (grayscale image) to 6, then 16, and so on.

Thus, the purpose of padding in LeNet-5 goes beyond merely avoiding an increase in channels; it serves critical roles in maintaining spatial dimensions and effectively learning features.