  Increasing depth of convolutional neural networks (CNNs) is a highly
promising method of increasing the accuracy of the (CNNs). Increased CNN depth
will also result in increased layer count (parameters), leading to a slow
backpropagation convergence prone to overfitting. We trained our model
(Residual-CNDS) to classify very large-scale scene datasets MIT Places 205, and
MIT Places 365-Standard. The outcome result from the two datasets proved our
proposed model (Residual-CNDS) effectively handled the slow convergence,
overfitting, and degradation. CNNs that include deep supervision (CNDS) add
supplementary branches to the deep convolutional neural network in specified
layers by calculating vanishing, effectively addressing delayed convergence and
overfitting. Nevertheless, (CNDS) does not resolve degradation; hence, we add
residual learning to the (CNDS) in certain layers after studying the best place
in which to add it. With this approach we overcome degradation in the very deep
network. We have built two models (Residual-CNDS 8), and (Residual-CNDS 10).
Moreover, we tested our models on two large-scale datasets, and we compared our
results with other recently introduced cutting-edge networks in the domain of
top-1 and top-5 classification accuracy. As a result, both of models have shown
good improvement, which supports the assertion that the addition of residual
connections enhances network CNDS accuracy without adding any computation
complexity.
