Author(s) :

Sultan Khaibar Safi.

Volume/Issue :

Abstract :

- The remarkable success of Convolutional Neural Networks (CNNs) in image recognition and related tasks has been hampered by the ever-present challenge of overfitting and the pursuit of robust generalization performance. This article meticulously dissects and compares various regularization techniques specifically designed to empower deep learning for image tasks within the context of CNN architectures. We embark on a rigorous exploration of fundamental techniques like L1 and L2 regularization, delving into their theoretical foundations. We further unveil the intricacies of advanced methods such as Dropout, Data Augmentation, Early Stopping, and the synergistic approaches of Elastic Net and Group Lasso regularization. Through a meticulous examination, we unveil the theoretical underpinnings of these techniques, illuminate effective strategies for hyperparameter selection, and elucidate their profound impact on model complexity, weight sparsity, and ultimately, the network's ability to generalize effectively. To empirically validate these insights and solidify our comparative analysis, we conduct controlled experiments utilizing benchmark image datasets. This empirical validation process sheds light on the efficacy of each technique. By meticulously analyzing the trade-offs inherent in these diverse regularization approaches and their suitability for specific image data characteristics and CNN architectures, this article empowers researchers with a comprehensive understanding of these techniques. Armed with this knowledge, researchers can make informed decisions to optimize performance in deep learning tasks involving images, ultimately propelling the field towards ever-greater advancements.

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