Residual Connections Enable Deeper ResNet Training
Residual connections provide an identity path across layers. If added residual layers learn a zero residual mapping, the block preserves the identity mapping, so increasing depth does not force the network to learn a worse function. This makes much deeper ResNets easier to train, helps avoid the degradation observed in plain networks, and lets information propagate directly across layers.
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Residual Connections Enable Deeper ResNet Training
As plain networks grow deeper, their training accuracy can paradoxically worsen, a phenomenon known as the ___ problem.
Order the steps carried out during a forward pass in a residual block to compute the target function f(x) from an input x.
Explain why reformulating the layers to learn the residual mapping g(x) makes approximating the identity function f(x) = x easier for the network compared to learning f(x) directly.
Match each concept from residual learning to its corresponding description.