  Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art
performance on many visual recognition tasks. However, the combination of
convolution and pooling operations only shows invariance to small local
location changes in meaningful objects in input. Sometimes, such networks are
trained using data augmentation to encode this invariance into the parameters,
which restricts the capacity of the model to learn the content of these
objects. A more efficient use of the parameter budget is to encode rotation or
translation invariance into the model architecture, which relieves the model
from the need to learn them. To enable the model to focus on learning the
content of objects other than their locations, we propose to conduct patch
ranking of the feature maps before feeding them into the next layer. When patch
ranking is combined with convolution and pooling operations, we obtain
consistent representations despite the location of meaningful objects in input.
We show that the patch ranking module improves the performance of the CNN on
many benchmark tasks, including MNIST digit recognition, large-scale image
recognition, and image retrieval. The code is available at
https://github.com//jasonustc/caffe-multigpu/tree/TICNN .
