Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach has introduced an automatic method for creating high-quality datasets without manual effort, using hierarchical k-means clustering and balanced sampling. This approach enables training self-supervised models on automatically curated datasets, which alleviates the need for costly manual labeling and curation. The technique can be applied to various domains such as computer vision, earth observation, and natural language processing, improving model robustness and generalization by training on diverse and balanced datasets.