  Convolutional neural networks (CNNs) have achieved remarkable success in
image recognition. Although the internal patterns of the input images are
effectively learned by the CNNs, these patterns only constitute a small
proportion of useful patterns contained in the input images. This can be
attributed to the fact that the CNNs will stop learning if the learned patterns
are enough to make a correct classification. Network regularization methods
like dropout and SpatialDropout can ease this problem. During training, they
randomly drop the features. These dropout methods, in essence, change the
patterns learned by the networks, and in turn, forces the networks to learn
other patterns to make the correct classification. However, the above methods
have an important drawback. Randomly dropping features is generally inefficient
and can introduce unnecessary noise. To tackle this problem, we propose
SelectScale. Instead of randomly dropping units, SelectScale selects the
important features in networks and adjusts them during training. Using
SelectScale, we improve the performance of CNNs on CIFAR and ImageNet.
