Data driven designing of convolutional neural networks architectures for image classification

Authors: Kawa, S.A. and Wani, M.A.

Journal: Engineering Letters

Volume: 31

Issue: 2

Pages: 735-742

eISSN: 1816-0948

ISSN: 1816-093X

Abstract:

The process of designing Convolutional Neural Networks (CNN) architecture has been automated using Neural Architecture Search (NAS) algorithms, however, these algorithms pose a significant computational demand due to the large search space of possible architectures. This paper proposes a two-stage algorithm to address the computational resource demand of NAS by generating architectures directly from the dataset. In the first stage, the complexity tree is generated from the dataset based on the complexity of individual images. The image complexity is determined by color variation and changes in color intensity. This complexity tree is then utilized in the second stage for designing the CNN architecture. As the complexity tree solely necessitates one-time generation for the dataset, our proposed model does not have significant computational demands. In the experiment that was performed utilizing standard benchmark datasets, the generated (CNN) models attained an accuracy of 97.35% on CIFAR-10 and 72.57% on CIFAR-100 datasets. Our proposed model was compared with state-of-the-art models and yielded a notable improvement in accuracy. Furthermore, it outperformed all other models in terms of computational resource requirement.

Source: Scopus