Enhancing Neural Network Performance Through Self-Supervised Learning and Data Augmentation Techniques

Abstract

The increasing complexity of neural networks requires advanced techniques to ensure efficient training and high performance. This paper explores the integration of self-supervised learning with data augmentation strategies to enhance neural network generalization and accuracy in various tasks, such as image recognition and natural language processing. By leveraging unlabeled data and automatically generating meaningful features, self-supervised learning can overcome limitations in labeled datasets. Additionally, this study implements diverse data augmentation techniques to increase the robustness of models. The results demonstrate significant improvements in model performance, reduced overfitting, and greater resilience to noisy data. These findings highlight the potential of combining self-supervised learning with data augmentation for optimizing neural networks in real-world applications.