用自适应量化压缩数据集，省存储又不丢精度。
Adaptive Dataset Quantization: A New Direction for Dataset Pruning
- 对每张图内部冗余信息做比特级压缩，保留关键特征。
- 在相同压缩比下，模型训练准确率接近原始数据集。
- 适合边缘设备部署，尤其资源受限场景下的数据存储。
本文针对资源受限的边缘设备在大规模数据集存储与通信成本上的挑战，提出一种新型数据集量化方法，以减少样本内冗余。与传统关注样本间冗余的数据剪枝和知识蒸馏不同，该方法通过压缩单个图像中的冗余或低信息内容来实现压缩，同时保留必要特征。首先对每个样本应用线性对称量化获得初始量化范围和缩放系数；随后引入自适应量化分配算法，根据不同样本的精度需求分配不同的量化比例，保持整体压缩比恒定。主要贡献包括：(1) 首次使用有限比特表示数据集实现存储压缩；(2) 提出具有自适应比例分配的数据集层级量化算法；(3) 在CIFAR-10、CIFAR-100和ImageNet-1K上进行大量实验验证有效性。结果表明，该方法在维持模型训练性能的同时实现显著数据集压缩，在相同压缩比下优于传统量化与数据剪枝基线。
原文摘要 · Abstract (English)
This paper addresses the challenges of storage and communication costs for large-scale datasets in resource-constrained edge devices by proposing a novel dataset quantization approach to reduce intra-sample redundancy. Unlike traditional dataset pruning and distillation methods that focus on inter-sample redundancy, the proposed method compresses each image by reducing redundant or less informative content within samples while preserving essential features. It first applies linear symmetric quantization to obtain an initial quantization range and scale for each sample. Then, an adaptive quantization allocation algorithm is introduced to distribute different quantization ratios for samples with varying precision requirements, maintaining a constant total compression ratio. The main contributions include: (1) being the first to use limited bits to represent datasets for storage reduction; (2) introducing a dataset-level quantization algorithm with adaptive ratio allocation; and (3) validating the method's effectiveness through extensive experiments on CIFAR-10, CIFAR-100, and ImageNet-1K. Results show that the method maintains model training performance while achieving significant dataset compression, outperforming traditional quantization and dataset pruning baselines under the same compression ratios.
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