Curated Data
for Efficient
Learning.

The ECCV 2026 Workshop on Curated Data for Efficient Learning (CDEL) seeks to advance the understanding and development of data-centric techniques that improve the efficiency of training large-scale machine learning models. As model sizes continue to grow and data requirements scale accordingly, this workshop brings attention to the increasingly critical role of data quality, selection, and synthesis in achieving high model performance with reduced computational cost.

Rather than chasing ever-larger datasets and models, CDEL focuses on curating and distilling high-value data, drawing on techniques such as dataset distillation, data pruning, synthetic data generation, and sampling optimization. These approaches aim to reduce redundancy, improve generalization, and make learning possible even when data is scarce.