Inverted dropout is a variant of the original dropout technique developed by Hinton et al.
Just like traditional dropout, inverted dropout randomly keeps some weights and sets others to zero. This is known as the “keep probability” \(p\).
The one difference is that, during the training of a neural network, inverted dropout scales the activations by the inverse of the keep probability \(q = 1 - p\).
This prevents network’s activations from getting too large, and does not require any changes to the network during evaluation.
In contrast, traditional dropout requires scaling to be implemented during the test phase.