  In deeplearning models the FC (fully connected) layer has biggest important
role for classification of the input based on the learned features from
previous layers. The FC layers has highest numbers of parameters and
fine-tuning these large numbers of parameters, consumes most of the
computational resources, so in this paper it is aimed to reduce these large
numbers of parameters significantly with improved performance. The motivation
is inspired from SpinalNet and other biological architecture. The proposed
architecture has a gradient highway between input to output layers and this
solves the problem of diminishing gradient in deep networks. In this all the
layers receives the input from previous layers as well as the CNN layer output
and this way all layers contribute in decision making with last layer. This
approach has improved classification performance over the SpinalNet
architecture and has SOTA performance on many datasets such as Caltech101,
KMNIST, QMNIST and EMNIST. The source code is available at
https://github.com/praveenchopra/ProgressiveSpinalNet.
