Self-Supervised Learning in Generative AI: Enhancing Model Efficiency and Adaptability for Limited Data Scenarios

Abstract

Recent advancements in Generative AI and deep learning have demonstrated remarkable capabilities in producing high-quality, human-like outputs across domains such as text, image, and audio generation. However, these models often rely on extensive labeled datasets, which can be resource-intensive to curate. This research focuses on the integration of self-supervised learning (SSL) paradigms into Generative AI to address the challenges of limited data availability, domain adaptation, and computational efficiency.

The proposed study explores novel SSL techniques tailored for generative tasks, including contrastive learning, masked prediction, and clustering-based approaches. It examines their impact on enhancing model generalization, reducing overfitting, and improving robustness against adversarial attacks. Additionally, it investigates hybrid architectures combining SSL with transformer-based and diffusion models to optimize performance across diverse generative applications. By leveraging SSL, this research aims to bridge the gap between data efficiency and generative quality, paving the way for more accessible and adaptable AI systems capable of thriving in real-world, data-sparse environments.