Info-Coevolution: An Efficient Framework for Data Model Coevolution

• URL: http://arxiv.org/abs/2506.08070v2
• Date: Fri, 20 Jun 2025 02:52:55 GMT
• Title: Info-Coevolution: An Efficient Framework for Data Model Coevolution
• Authors: Ziheng Qin, Hailun Xu, Wei Chee Yew, Qi Jia, Yang Luo, Kanchan Sarkar, Danhui Guan, Kai Wang, Yang You,
• Abstract summary: We propose a novel framework that enables models and data to coevolve through online selective annotation with no bias.<n>For real-world datasets like ImageNet-1K, Info-Coevolution reduces annotation and training costs by 32% without performance loss.
• Score: 11.754869657967207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 at https://github.com/NUS-HPC-AI-Lab/Info-Coevolution/.

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