Dropout
A regularizer that drops each unit with probability p on every training pass (and rescales the survivors), which acts like training a large ensemble of thinned networks. At inference all units are kept.
Example
With p = 0.5 on a 512-unit layer, each training step uses a different random ~256 units, so features must be useful on their own rather than co-adapted.