Machine learning relies heavily on data, yet the continuous growth of real-world data poses challenges for efficient dataset construction and training. A fundamental yet unsolved question is: given our current model and data, does a new data (sample/batch) need annotation/learning? Conventional approaches retain all available data, leading to non-optimal data and training efficiency. Active learning aims to reduce data redundancy by selecting a subset of samples to annotate, while it increases pipeline complexity and introduces bias. In this work, we propose Info-Coevolution, a novel framework that efficiently enables models and data to coevolve through online selective annotation with no bias. Leveraging task-specific models (and open-source models), it selectively annotates and integrates online and web data to improve datasets efficiently. For real-world datasets like ImageNet-1K, Info-Coevolution reduces annotation and training costs by 32\% without performance loss. It is able to automatically give the saving ratio without tuning the ratio. It can further reduce the annotation ratio to 50\% with semi-supervised learning. We also explore retrieval-based dataset enhancement using unlabeled open-source data. Code is available atthis https URL.
View on arXiv@article{qin2025_2506.08070,
title={ Info-Coevolution: An Efficient Framework for Data Model Coevolution },
author={ Ziheng Qin and Hailun Xu and Wei Chee Yew and Qi Jia and Yang Luo and Kanchan Sarkar and Danhui Guan and Kai Wang and Yang You },
journal={arXiv preprint arXiv:2506.08070},
year={ 2025 }
}