Translations:Dropout/24/en

Spatial dropout (Tompson et al., 2015) is particularly important for convolutional networks. Standard dropout on convolutional feature maps is ineffective because adjacent activations are highly correlated; dropping individual pixels still leaves redundant spatial information. Spatial dropout instead drops entire channels, forcing the network to use diverse feature representations.