In this paper , the authors introduce a new network structure for the traditional CNN to better extract and interpret latent features. It is named "Network In Network (NIN)". NIN vs tradional CNN In a traditional CNN, convolutional layers and spatial pooling layers are stacked followed by fully connected layers and an output layer. The convolution layers generate feature maps by linear convolutional filters followed by non-linear activation functions. The NIN structure addresses the following 2 limitations of a traditional CNN. Kernels/filters used for each CNN layer works well when the features to be extracted are linearly separable. Fully connected layers at the end of the CNN leads to over-fitting the training data. Convolution with linear filter vs Neural network The convolution layers involve a kernel that slides over the previous field (input or layers) and extracts features. The kernel is usually a matrix with which convolution is done. This is a linear operation. Mea