DeCO通过保留关键局部特征，提升细粒度图像压缩效果。
DeCO: Discriminative Evidence Composition for Fine-Grained Dataset Distillation
- 用预训练模型定位重要图像区域，构建类内证据库
- 在不同每类图像数（IPC）下均超越现有方法
- 适合需要高精度细粒度分类的轻量化场景
数据蒸馏将大规模训练集压缩为紧凑的合成数据集，同时保持其下游实用性。然而，现有方法主要关注全局图像统计，可能忽略细粒度视觉分类（FGVC）所需的局部证据，如物体部件、细微纹理和区域特定结构。本文将细粒度数据蒸馏建模为受限的判别性证据保留问题，提出判别性证据组合（DeCO）。DeCO利用预训练TransFG教师模型的注意力传播识别信息丰富块，通过空间多样化减少冗余覆盖，并将所得区域组织成类别专属的证据库。同类别区域被打包为紧凑的网格合成图像。教师仅用于数据构建，下游学生使用标准硬标签监督训练，无需教师输出。在CUB-200-2011、FGVC-Aircraft和Stanford Cars上的实验表明，DeCO在不同每类图像数（IPC）预算下持续优于代表性核心集和数据蒸馏基线。
原文摘要 · Abstract (English)
Dataset distillation compresses a large training set into a compact synthetic set while preserving its downstream utility. However, existing methods primarily preserve global image statistics and may overlook the localized evidence essential for fine-grained visual classification (FGVC), such as object parts, subtle textures, and region-specific structures. We formulate fine-grained dataset distillation as budgeted discriminative-evidence preservation and propose Discriminative Evidence Composition (DeCO). DeCO uses attention rollout from a pretrained TransFG teacher to identify informative patches, applies spatial diversification to reduce redundant coverage, and organizes the resulting regions into class-wise evidence banks. Multiple same-class regions are then packed into compact grid-composed images. The teacher is used only for dataset construction, whereas downstream students are trained with standard hard-label supervision without teacher logits. Experiments on CUB-200-2011, FGVC-Aircraft, and Stanford Cars show that DeCO consistently outperforms representative coreset and dataset-distillation baselines under different IPC budgets.
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