Self-Optimisation of Dense Neural Network Architectures: An Incremental Approach

This paper presents a newly developed self-structuring algorithm for generating convolutional neural networks, as well as the results of preliminary tests performed on it. The algorithm produces DenseNet and DenseNet-BC architectures layer by layer from scratch, at the same time as they are being trained. Experimental results for well-known image classification datasets (CIFAR-10 and SVHN) are promising. The accuracy levels of generated networks are not significantly different than those of prebuilt DenseNet and DenseNet-BC with similar topologies, and are approaching the state of the art for these datasets.