Analysis of MLP, CNN,and Transfer Learning Using VGG-16 for CIFAR-10 Dataset

Shahid Mahmood ()
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Shahid Mahmood: Bahria University Islamabad, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 4, 1826-1838

Abstract: Artificial Neural Networks (ANN) are becoming the core domain of Artificial Intelligence. Generally, Machine learning and specifically, deep learning gained popularity in problem-solving 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 approachesusing 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.

Keywords: Artificial Neural Networks (ANN); Convolutional Neural Network (CNN); Multi-Layer Perceptron (MLP); CIFAR-10 Dataset; VGG-16 (search for similar items in EconPapers)
Date: 2024
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