A Comprehensive Survey: Harmonizing Transfer Learning and Self‐Supervised Learning for Enhanced Performance in Machine Learning and Deep Learning

Abstract

In recent years, the integration of transfer learning (TL) and self‐supervised learning (SSL) has gained considerable attention as an effective strategy for improving model performance across a wide range of machine learning (ML) and deep learning (DL) tasks. TL enables the reuse of knowledge acquired from pretrained models to accelerate learning in new tasks, while SSL exploits large volumes of unlabeled data to learn meaningful representations without manual annotation. This article presents a systematic and comprehensive survey of existing approaches that harmonize SSL with TL. The survey reviews key mechanisms such as self‐supervised pre‐training, feature extraction, fine‐tuning strategies, and domain adaptation across both ML and DL paradigms, including convolutional neural networks, recurrent neural networks, transformer‐based architectures, and classical ML models. By synthesizing findings reported in the literature, this study highlights how the combination of self‐supervised representation learning and TL enhances generalization, robustness, and data efficiency. Furthermore, the survey discusses representative application domains, emerging trends, and comparative insights across benchmark tasks. Finally, open challenges and future research directions are outlined, emphasizing scalability, domain shift handling, and the role of foundation models. This survey aims to provide researchers and practitioners with a structured understanding of current progress and opportunities in harmonizing TL and SSL.