  Currently, increasingly deeper neural networks have been applied to improve
their accuracy. In contrast, We propose a novel wider Convolutional Neural
Networks (CNN) architecture, motivated by the Multi-column Deep Neural Networks
and the Network In Network(NIN), aiming for higher accuracy without input data
transmutation. In our architecture, namely "CNN In Convolution"(CNNIC), a small
CNN, instead of the original generalized liner model(GLM) based filters, is
convoluted as kernel on the original image, serving as feature extracting layer
of this networks. And further classifications are then carried out by a global
average pooling layer and a softmax layer. Dropout and orthonormal
initialization are applied to overcome training difficulties including slow
convergence and over-fitting. Persuasive classification performance is
demonstrated on MNIST.
