Very deep networks often result in gradients that vanishes as the gradient is back-propagated to earlier layers, repeated multiplication may make the gradient infinitely small. ResNet uses the concept of residual blocks that include shortcut skip connections to jump over some layers.

The authors of ResNet paper provide evidence showing that residual networks are easier to optimize, and can gain accuracy from considerably increased depth by reformulating the layers as learning residual functions with reference to the layer inputs.

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