A technique used in deep learning to help mitigate the vanishing gradient problem and allow for training of deeper networks. The idea is to create shortcut connections that skip one or more layers, allowing the gradient to flow directly through these connections during backpropagation. This helps to preserve the signal and allows for better training of deeper networks.
The main idea behind residual connections is to reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. This can be expressed mathematically as: Where:
- is the output of the -th layer.
- is the identity mapping (the input to the layer).
- is the residual function that the layer is learning
def residual_block(x):
# x is the input to the block
residual = x # Save the input for the skip connection
out = some_layer(x) # Apply some transformation to the input
out += residual # Add the input back to the output (skip connection)
return out