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Research Paper | Computer Science | India | Volume 14 Issue 2, February 2025 | Popularity: 5.7 / 10

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In Image Recognition: Are Deep Neural Networks Always Necessary, or Can Shallow Neural Networks Serve the Purpose Sometimes?

Dr. Ashok Jahagirdar

Abstract: Image recognition has become a cornerstone of modern artificial intelligence (AI) applications Image recognition has emerged as a critical component of artificial intelligence (AI), enabling machines to interpret and classify visual data with remarkable accuracy. Deep neural networks (DNNs), particularly convolutional neural networks (CNNs), have become the de facto standard for image recognition tasks due to their ability to learn hierarchical features and achieve state-of-the-art performance on complex datasets. Their success has led to widespread adoption in applications ranging from autonomous vehicles to medical diagnostics. However, the deployment of DNNs is not without challenges - they demand substantial computational resources, extensive training times, and large datasets, which can be prohibitive in resource-constrained environments or for simpler tasks. This raises a critical question: Are deep neural networks always indispensable for image recognition, or can shallow neural networks (SNNs) sometimes achieve comparable results with significantly fewer resources? This paper investigates the effectiveness of shallow neural networks in image recognition tasks and compares their performance to deep neural networks across various datasets. Our findings suggest that shallow networks can indeed serve as a viable alternative in specific scenarios, offering significant reductions in computational overhead without substantial sacrifices in accuracy. This paper investigates the efficacy of shallow neural networks in image recognition tasks and compares their performance to that of deep neural networks across a range of datasets with varying complexity levels. We conduct a series of experiments using benchmark datasets such as MNIST, CIFAR-10, and a subset of ImageNet, evaluating both accuracy and computational efficiency. Our findings reveal that shallow networks can indeed serve as a viable alternative in specific scenarios, particularly for tasks with limited complexity or in environments where computational resources are constrained. For instance, on the MNIST dataset, shallow networks achieve accuracy levels exceeding 98%, closely matching the performance of deep networks. On more complex datasets like CIFAR-10, while shallow networks exhibit slightly lower accuracy (~85% compared to ~92% for deep networks), they offer significant advantages in terms of reduced training time and memory usage. The implications of this research are profound, suggesting that practitioners need not always resort to deep networks for image recognition tasks. By carefully assessing the complexity of the task and the available resources, it is possible to leverage shallow networks to achieve a balance between performance and efficiency. This study not only highlights the potential of shallow networks but also underscores the importance of tailoring model complexity to the specific requirements of the task at hand. Future research directions could explore hybrid models that combine the strengths of both shallow and deep networks, as well as the development of lightweight architectures optimized for specific applications.

Keywords: image recognition, artificial intelligence, deep neural networks, convolutional neural networks, machine learning

Edition: Volume 14 Issue 2, February 2025

Pages: 1496 - 1500

DOI: https://www.doi.org/10.21275/SR25223141136

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