Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Finally, to illustrate the effect of the skip connection on an image, I’ve passed one of
the images from the Rock Paper Scissors dataset through a randomly initialized
residual block (three channels in and out, no downsampling), with and without a
skip connection. These are the results.
Figure 7.11 - Skip connection in action
On the one hand (that’s a good pun, c’mon!), if there are no skip connections, some
information may be lost, like the different shades on the back of the hand. On the
other hand (sorry!), skip connections may help to preserve that information.
That’s the general idea behind the ResNet model. Of course, the whole
architecture is more complex than that, involving stacking many different residual
blocks and adding some more bells and whistles to the mix. We’re not going into
any more details here, but the pre-trained models can easily be used for transfer
learning, just like we did with the AlexNet model.
Putting It All Together
In this chapter, we’ve gone through the necessary steps to use transfer learning
with pre-trained models for computer vision tasks: using ImageNet statistics for
pre-processing the inputs, freezing layers (or not), replacing the "top" layer, and
optionally speeding up training by generating features and training the "top" of
the model independently.
554 | Chapter 7: Transfer Learning