Faster Convolutional Neural Networks

Abstract

There exists a gap between the computational cost of state of the art image processing models and the processing power of publicly available devices. This gap is reducing the applicability of these promising models. Trying to bridge this gap, first we investigate pruning and factorization to reduce the computational cost of a model. Secondly, we look for alternative convolution operations to design state of the art models. Thirdly, using these alternative convolution operations, we train a model for the CIFAR-10 classifi cation task. Our proposed model achieves comparable results (91:1% top-1 accuracy) to ResNet-20 (91:25% top-1 accuracy) with half the model size and one-third floating point operations. Finally, we apply pruning and factorization and observe that these methods are ineffective in reducing the computational complexity and preserving the accuracy of our proposed model.