A convolutional neural network (CNN) can perform well in a variety of applications such as human face gender classification, but requiring flips of convolutional kernels in implementation. By replacing convolution with correlation, we propose a correlational neural network (CorNN) instead of a CNN. A CorNN takes advantage over a CNN in that it requires no flips of correlational kernels in implementation, saving a lot of training and testing time. Experimental results show that an 8-layer CorNN

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... or gender classification can not only perform as well as the corresponding CNN, but also run surprisingly faster with a relative reduction of 11.29%~18.83% training time, and 10.16%~16.57% testing time.