BRIDGING DEEP LEARNING AND ADAPTIVE SYSTEMS: A PERFORMANCE STUDY ON CIFAR-10 IMAGE CLASSIFICATION

Image classification is a fundamental task in computer vision, with applications spanning from medical diagnostics to autonomous driving. This study presents a comparative analysis of Convolutional Neural Networks (CNNs) and a representative adaptive system approach, specifically K-Nearest Neighbors (KNN), for image classification on the CIFAR-10 dataset. CNNs, known for their hierarchical feature learning capabilities, have revolutionized the field, while adaptive systems like KNN represent a class of algorithms that dynamically adjust their decision boundaries based on data relationships. The CIFAR-10 dataset, comprising 60,000 32x32 color images across 10 classes, serves as the benchmark [1]. Our methodology involves training a custom CNN architecture and applying KNN, with careful consideration of preprocessing and hyperparameter tuning for both models. Performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results indicate that CNNs significantly outperform the KNN approach on this dataset, demonstrating their superior ability to extract and learn complex, invariant features from raw image data. This research highlights the inherent strengths of deep learning architectures in handling the intricacies of visual data while also providing insights into the characteristics where simpler adaptive systems might fall short or excel.