The Zero-padded Shortcut Connection is a type of residual connection that is utilized in the PyramidNet architecture. PyramidNets use residual connections to enable deeper networks while preventing the accuracy from degrading, and the zero-padded method is one of the techniques they use.
What is a residual connection?
Residual connections, also known as skip connections, are designed to solve the problem of vanishing gradients. Vanishing gradients occur when the gradient of a loss function goes to zero. When this happens, it is difficult to train the deeper layers of a neural network. Residual connections circumvent this issue by adding the inputs of a certain layer to the outputs of that same layer. Essentially, they are shortcuts that jump over a troubled layer and pass information to the next layer. This improves the flow of information and gradients, allowing for deeper networks.
What is the PyramidNet architecture?
The PyramidNet architecture is a type of deep neural network comprised of a pyramid-like structure of residual units or blocks. Each block consists of several layers, and each layer is a series of convolutional, batch normalization, and activation operations. The primary difference between PyramidNets and traditional residual networks (ResNets) is that the feature map sizes increase and decrease in a bottom-up and top-down manner, creating a pyramid structure.
Why use a zero-padded shortcut connection?
In traditional residual networks, an identity mapping is used as a shortcut connection, which is a simple way to provide a shortcut between different layers. However, in PyramidNets, the identity mapping alone cannot be used because the feature map dimension differs among individual residual units. This is where the zero-padded shortcut or projection shortcut comes into the picture as a solution. However, using the projection shortcut can cause optimization issues, especially for very deep networks, since it requires additional parameters. The zero-padded shortcut, on the other hand, is easy to implement, does not require extra parameters, and avoids the overfitting problem.
How does the zero-padded shortcut work?
The zero-padded shortcut works by adding a zero-padded matrix to the shortcut connection. When the output feature map size is smaller than the input feature map size, a zero-padded matrix is added to the input so that the sizes match. This allows for the addition operation to be performed, which is the fundamental operation of residual connections. The addition operation adds the zero-padded input and output, resulting in an output feature map size that is the same as the input feature map size.
The zero-padded shortcut connection is a simple and effective way to provide a shortcut between different layers in a deep neural network. It helps mitigate the optimization problems that can arise from using projection shortcuts and prevents overfitting. When combined with the PyramidNet architecture, the zero-padded shortcut connection allows for training very deep neural networks that are both accurate and efficient.
