  Dropout regularization has been widely used in deep learning but performs
less effective for convolutional neural networks since the spatially correlated
features allow dropped information to still flow through the networks. Some
structured forms of dropout have been proposed to address this but prone to
result in over or under regularization as features are dropped randomly. In
this paper, we propose a targeted regularization method named TargetDrop which
incorporates the attention mechanism to drop the discriminative feature units.
Specifically, it masks out the target regions of the feature maps corresponding
to the target channels. Experimental results compared with the other methods or
applied for different networks demonstrate the regularization effect of our
method.
