Self-supervised CondenseNet for feature learning to increase the accuracy in image classification

Deep learning methods are leveraged in various computer science and artificial intelligence areas, including image classification. Convolutional neural network (CNN) is one of the most widely used deep neural networks for which, several highly effective architectures for image classification have been presented. In this paper, an improved version of the recently introduced CondenseNet is provided as a new network architecture. On the other hand, due to the necessity of reducing the dependence on labeled data in the training process of neural networks, a self-supervised learning method is also proposed for labeling unlabeled images. The results of the experiments show the proper performance of the proposed self-supervised CondenseNet method compared to the basic version of CondenseNet. The experiments are conducted on CIFAR_10 and CIFAR-100 datasets and show better accuracy of the proposed method.