  This paper presents a new version of Dropout called Split Dropout (sDropout)
and rotational convolution techniques to improve CNNs' performance on image
classification. The widely used standard Dropout has advantage of preventing
deep neural networks from overfitting by randomly dropping units during
training. Our sDropout randomly splits the data into two subsets and keeps both
rather than discards one subset. We also introduce two rotational convolution
techniques, i.e. rotate-pooling convolution (RPC) and flip-rotate-pooling
convolution (FRPC) to boost CNNs' performance on the robustness for rotation
transformation. These two techniques encode rotation invariance into the
network without adding extra parameters. Experimental evaluations on
ImageNet2012 classification task demonstrate that sDropout not only enhances
the performance but also converges faster. Additionally, RPC and FRPC make CNNs
more robust for rotation transformations. Overall, FRPC together with sDropout
bring $1.18\%$ (model of Zeiler and Fergus~\cite{zeiler2013visualizing},
10-view, top-1) accuracy increase in ImageNet 2012 classification task compared
to the original network.
