  In convolutional neural network (CNN), dropout cannot work well because
dropped information is not entirely obscured in convolutional layers where
features are correlated spatially. Except randomly discarding regions or
channels, many approaches try to overcome this defect by dropping influential
units. In this paper, we propose a non-random dropout method named
FocusedDropout, aiming to make the network focus more on the target. In
FocusedDropout, we use a simple but effective way to search for the
target-related features, retain these features and discard others, which is
contrary to the existing methods. We found that this novel method can improve
network performance by making the network more target-focused. Besides,
increasing the weight decay while using FocusedDropout can avoid the
overfitting and increase accuracy. Experimental results show that even a slight
cost, 10\% of batches employing FocusedDropout, can produce a nice performance
boost over the baselines on multiple datasets of classification, including
CIFAR10, CIFAR100, Tiny Imagenet, and has a good versatility for different CNN
models.
