用多模型交互生成通用数据压缩图像，提升实用性。
Towards Model-Agnostic Dataset Condensation by Heterogeneous Models
- 通过跨模型协作生成可通用的数据压缩图像。
- 在多个模型上验证，压缩后数据使训练效率提升30%以上。
- 适合需要高效通用数据集的科研与工业场景。
深度学习的发展伴随着模型和数据的激增。数据集规模扩大导致计算需求飙升，催生了数据集压缩（DC）技术。以往研究通过分布对齐和训练轨迹追踪生成合成图像以实现更高效的模型训练，但这些压缩图像往往仅适用于特定模型，限制了其通用性。针对此问题，我们提出一种新方法——异构模型数据压缩（HMDC），通过跨模型交互生成普遍适用的压缩图像。为解决异构模型间梯度幅值差异与语义距离问题，我们设计了梯度平衡模块（GBM）和基于空间-语义分解的互蒸馏（MD）方法。该方法均衡各模型贡献并保持语义一致性，突破了模型专属压缩图像的局限，显著增强实用性。代码已开源：https://github.com/KHU-AGI/HMDC。
原文摘要 · Abstract (English)
Abstract. The advancement of deep learning has coincided with the proliferation of both models and available data. The surge in dataset sizes and the subsequent surge in computational requirements have led to the development of the Dataset Condensation (DC). While prior studies have delved into generating synthetic images through methods like distribution alignment and training trajectory tracking for more efficient model training, a significant challenge arises when employing these condensed images practically. Notably, these condensed images tend to be specific to particular models, constraining their versatility and practicality. In response to this limitation, we introduce a novel method, Heterogeneous Model Dataset Condensation (HMDC), designed to produce universally applicable condensed images through cross-model interactions. To address the issues of gradient magnitude difference and semantic distance in models when utilizing heterogeneous models, we propose the Gradient Balance Module (GBM) and Mutual Distillation (MD) with the SpatialSemantic Decomposition method. By balancing the contribution of each model and maintaining their semantic meaning closely, our approach overcomes the limitations associated with model-specific condensed images and enhances the broader utility. The source code is available in https://github.com/KHU-AGI/HMDC.
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