8.6. Residual Networks (ResNet) and ResNeXt — Dive into Deep Learning 1.0.3 documentation thumbnail
8.6. Residual Networks (ResNet) and ResNeXt — Dive into Deep Learning 1.0.3 documentation
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more training data would offer distinct benefit in closing the gap and improving accuracy. portion within the dotted-line box needs to learn the residual mapping inputs can forward propagate faster through the residual connections across layers only if larger function classes contain the smaller one
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  • more training data would offer distinct benefit in closing the gap and improving accuracy.
  • portion within the dotted-line box needs to learn the residual mapping
  • inputs can forward propagate faster through the residual connections across layers
  • only if larger function classes contain the smaller ones are we guaranteed that increasing them strictly increases the expressive power of the network.
  • The right figure illustrates the residual block of ResNet, where the solid line carrying the layer input � to the addition operator is called a residual connection (or shortcut connection). With residual blocks, inputs can forward propagate faster through the residual connections across layers. In fact, the residual block can be thought of as a s...

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