Self-Supervised Learning: How to Unlock Data Efficiency, Reduce Labeling Costs, and Improve Transferability
Self-supervised learning: unlocking data efficiency in machine learning Self-supervised learning has quickly become a central strategy for training models that learn useful representations from unlabeled data. Instead of relying on large labeled datasets, self-supervised methods create predictive tasks from raw inputs—letting models discover structure and patterns that transfer well to downstream tasks. How it worksSelf-supervised […]