Enhancing Convolutional Neural Networks through Differentiable Architecture Search (DARTS): A Novel Optimization Approach

Convolutional Neural Networks (CNNs) have revolutionized various fields by achieving stateof-the-art performance in image processing, natural language processing, and other domains. However, designing effective CNN architectures typically requires extensive expertise and computational resources. In this research, we propose a novel optimization approach leveraging Differentiable Architecture Search (DARTS) to automate the architecture design process. DARTS employs a continuous relaxation of the architecture search space, enabling gradient-based optimization techniques to efficiently identify optimal network structures. To evaluate the efficiency of the approach, an experimental study was conducted using various benchmark datasets, like CIFAR-10, CIFAR-100, and ImageNet. Our results demonstrate that DARTS significantly reduces the computational cost and human effort involved in CNN design while maintaining or surpassing the performance of manually crafted architectures.