Exploring Self-Supervised Learning Architectures for Image Processing: Milestones and Challenges

Self-Supervised Learning (SSL) is a deep learning paradigm, having the capacity to learn from unlabeled datasets. SSL has demonstrated effectiveness in various applications of computer vision and image processing. The study examines milestone advancements in SSL methodologies applied to image processing, with particular attention to datasets, pretext tasks and different architectures that have been implemented to perform representation learning from unlabeled data. The persistent challenges in SSL are also discussed. Newer models, such as DINOv2 and iBOT, along with hybrid approaches has offered improvements in feature extraction and task adaptability. As per the current trends, the domain of SSL has the potential to revolutionize image processing, by enabling more efficient and accurate training of model without extensive labeled data.