Residual-CNDS for Grand Challenge Scene Dataset
This work addresses performance issues in deep CNNs for large-scale scene classification, but it appears incremental as it builds on existing deep supervision methods by adding residual connections.
The paper tackled the problem of slow convergence, overfitting, and degradation in very deep convolutional neural networks by proposing Residual-CNDS, which combines residual learning with deep supervision. The models showed good improvement in top-1 and top-5 classification accuracy on MIT Places 205 and MIT Places 365-Standard datasets compared to other cutting-edge networks.
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.