Date of Publication : May 2026

Abstract: Self-Supervised Learning (SSL) leverages inherent data structures to generate self-supervised learning signals, enabling efficient representation learning from vast unlabeled datasets. This bypasses the need for extensive labeling, unlike supervised learning, and unlocks the potential of massive unlabeled resources. In recent years, there has been a rapid advancement in SSL methods for visual representation learning. These methods have achieved state-of-the-art results on various tasks, including image classification, object detection, and segmentation. The advancement in SSL has led to many benefits. First, it has made learning from much larger datasets possible, which can lead to better performance. Second, it has reduced the need for human labelling, which can be expensive and time-consuming. Third, it has made it possible to learn from data that is not easily labeled, such as medical images or natural language. Advancement in SSL are still ongoing, and many challenges need to be addressed. One challenge is developing SSL methods to learn from more complex data. Another challenge is to develop SSL methods that can be used for more challenging tasks. Despite these challenges, the advancement in SSL is a promising direction for future research in machine learning. SSL has the potential to revolutionize how machine learning models are trained, and it can be used to solve a wide variety of problems. It also has the potential to make machine learning more accessible and affordable. This paper surveys the advancements in self-supervised learning for visual representation learning. Through an in-depth analysis of recent research, the paper contrasts various approaches, evaluates their strengths and limitations, and offers insights into their contributions to enhancing visual representation learning. By examining key approaches, data augmentation strategies, and loss functions, the paper assesses the strengths and limitations of each methodology.

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