ResCNN: An alternative implementation of Convolutional Neural Networks

Convolutional Neural Networks (CNN) have been used for long for feature extraction from images in deep learning. Here we introduce ResilientCNN or ResCNN for short where we show that when convolution is implemented as an matrix-matrix operation coupled with some image processing techniques like Singular Value Decomposition (SVD) can be used as an better alternative to traditional convolution. We show that our ResCNN learns with bigger batch sizes and at much higher learning rates (7x) without compromising on accuracy compared to traditional convolutional networks by implementing both models on the MNIST Dataset.