How does resnet model restores the skipped layers as it learns the feature space?
In the architecture of resnet there are skip connections between certain blocks. Now, the significance of these skip connections is that during the initial training weights are not that significant and due to multiple hidden layers we face the problem of vanishing gradients. To deal with this researchers introduced residual connection which connects the output of the previous block directly to the output of the next block and thus information keeps on transmitting. Like, just see the architecture of the residual network:

  Ref: https://missinglink.ai/guides/pytorch/pytorch-resnet-building-training-scaling-residual-networks-pytorch/


In VGG-19 the information is directly being transferred from one hidden layer and in ResNet the output of one block is being transferred to next block's input as well as to the ouput of the next block, so that if we face the vanishing gradients problem due to the hidden layers of this block network can deal with it.

To your query of why does the activation is not skipped, the output of both previous and next block is concatenated and then passed through an activation layer, that's why it doesn't skips the activation layer.