让无标签数据自己‘进化’，提升模型训练效率与可持续性
Collaborative Unlabeled Data Optimization
- 将知识编码到数据中，突破模型参数的依赖瓶颈
- 在Tiny-ImageNet和ImageNet-1K上分别提升13.6%和6.8%准确率
- 支持并行处理，训练速度最高提升1.94倍，适合大规模训练场景
本文提出一种全新的以数据为中心的范式，旨在通过优化无标签数据来提升深度学习训练的效率与可持续性。现有模型中心方法存在三大局限，根源在于：从数据中提取的知识被锁定在模型参数中，难以复用与扩展。为此，我们提出CoOpt框架，一种高效、可并行化的无标签数据协同优化方法，将知识直接编码至数据本身。通过分发无标签数据并利用公开的任务无关模型，CoOpt实现了可扩展、可复用、可持续的训练流程。在多种数据集与模型架构上的实验表明，该方法在Tiny-ImageNet和ImageNet-1K上分别取得13.6%和6.8%的性能提升，训练速度分别加快1.94倍和1.2倍。
原文摘要 · Abstract (English)
This paper pioneers a novel data-centric paradigm to maximize the utility of unlabeled data, tackling a critical question: How can we enhance the efficiency and sustainability of deep learning training by optimizing the data itself? We begin by identifying three key limitations in existing model-centric approaches, all rooted in a shared bottleneck: knowledge extracted from data is locked to model parameters, hindering its reusability and scalability. To this end, we propose CoOpt, a highly efficient, parallelized framework for collaborative unlabeled data optimization, thereby effectively encoding knowledge into the data itself. By distributing unlabeled data and leveraging publicly available task-agnostic models, CoOpt facilitates scalable, reusable, and sustainable training pipelines. Extensive experiments across diverse datasets and architectures demonstrate its efficacy and efficiency, achieving 13.6% and 6.8% improvements on Tiny-ImageNet and ImageNet-1K, respectively, with training speedups of $1.94 \times $ and $1.2 \times$.
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