Dropout
= Dropout
A training regularization method that randomly removes selected activations or connections. In inverted dropout with keep probability $p$, multiply each retained activation by $1/p$, so its conditional <expectation> equals the original activation; inference then uses the unmasked activations. The random masks discourage dependence on a particular small collection of activations.