A Correlational Neural Network for Gender Classification
Ting Zhang, Yujian Li, Haihe Hu, Zhang Yahong · 2017
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 for 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.