Fast Convolutional Neural Network with iterative and non-iterative learning

Abstract

Convolutional Neural Networks (CNNs) have achieved potentially good results for image classification. Due to their learning capabilities such networks are explored and implemented in real world applications. However, when it comes to training a CNN on large datasets without transfer learning, it takes long time and resources, due to the iterative process of weight update in fully connected layer. The backpropagation algorithm used to train the entire network suffers from slow convergence, getting trapped in a local minimum, not guaranteed to find global minimum and being hypersensitive to the learning rate set-up. Therefore, in this paper we propose a novel architecture of convolutional neural network with a non-iterative radial basis function-based classification layer which makes it more efficient for image classification tasks. This is a partially parameter free direct learning approach, which is highly beneficial in real-world applications as well as learning associated with large datasets. The proposed approach has been evaluated on four benchmark datasets such as CIFAR-10, MNIST, Digit and CE-MRI. It has achieved the accuracy scores of 99.8%, 99.8%, 98.4% and 98.3% on MNIST, Digit, CE-MRI and CIFAR-10 datasets respectively, when ResNet18 was used as backbone. Experimental results have illustrated that the proposed architecture is much better as it requires less parameter tuning, does not require handcrafted features and achieves same or higher accuracy in relatively lesser time than the standard CNN. Statistical significance tests were carried out to prove the efficiency of the proposed approach.