Analysis of MLP, CNN,and Transfer Learning Using VGG-16 for CIFAR-10 Dataset
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 4
Abstract
Artificial Neural Networks (ANN) are becoming the core domain of Artificial Intelligence. Generally, Machine learning and specifically, deep learning gained popularity in problemsolving by virtue of Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and transfer learning approach. Transfer learning is becoming a powerful and successful technique for a variety of computer vision and image analysis applications due to its capability of reusing well-known proven architectures and their weights. Identification of optimum architecture and classifier along with pre-trained architectures is one of the challenging tasks in achieving optimum accuracy in various image analysis tasks. This paper investigates the performance of MLP, CNN, and transfer learning approaches using VGG-16 by tweaking hyperparameters and classifier architecture. The investigations and critical analysis revealed that MLP and CNN architectures have achieved about 55 % and 80 % validation accuracy on test data. Further experiments using VGG-16 architecture with MLP as a classifier have achieved more than 93 % accuracy on standard specification hardware for image classification on the CIFAR-10 dataset.
Authors and Affiliations
Shahid Mahmood, Moneeb Gohar, Athar Waqar Alvi, Waleed Ahmad, Muhammad Kashif Khattak, Anum Mushtaq
Keywords
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