  Deep neural networks often work well when they are over-parameterized and
trained with a massive amount of noise and regularization, such as weight decay
and dropout. Although dropout is widely used as a regularization technique for
fully connected layers, it is often less effective for convolutional layers.
This lack of success of dropout for convolutional layers is perhaps due to the
fact that activation units in convolutional layers are spatially correlated so
information can still flow through convolutional networks despite dropout. Thus
a structured form of dropout is needed to regularize convolutional networks. In
this paper, we introduce DropBlock, a form of structured dropout, where units
in a contiguous region of a feature map are dropped together. We found that
applying DropbBlock in skip connections in addition to the convolution layers
increases the accuracy. Also, gradually increasing number of dropped units
during training leads to better accuracy and more robust to hyperparameter
choices. Extensive experiments show that DropBlock works better than dropout in
regularizing convolutional networks. On ImageNet classification, ResNet-50
architecture with DropBlock achieves $78.13\%$ accuracy, which is more than
$1.6\%$ improvement on the baseline. On COCO detection, DropBlock improves
Average Precision of RetinaNet from $36.8\%$ to $38.4\%$.
