FD$^2$: A Dedicated Framework for Fine-Grained Dataset Distillation

TL;DR

FD$^2$ introduces a specialized framework for fine-grained dataset distillation that enhances discriminative feature localization and representation, leading to improved performance on fine-grained datasets.

Contribution

The paper proposes FD$^2$, a novel framework that localizes discriminative regions and constructs fine-grained representations for dataset distillation, addressing limitations of existing methods on fine-grained data.

Findings

Improves distillation performance on fine-grained datasets

Seamlessly integrates with decoupled dataset distillation methods

Demonstrates strong transferability across multiple datasets

Abstract

Dataset distillation (DD) compresses a large training set into a small synthetic set, reducing storage and training cost, and has shown strong results on general benchmarks. Decoupled DD further improves efficiency by splitting the pipeline into pretraining, sample distillation, and soft-label generation. However, existing decoupled methods largely rely on coarse class-label supervision and optimize samples within each class in a nearly identical manner. On fine-grained datasets, this often yields distilled samples that (i) retain large intra-class variation with subtle inter-class differences and (ii) become overly similar within the same class, limiting localized discriminative cues and hurting recognition. To solve the above-mentioned problems, we propose FD$^{2}$, a dedicated framework for Fine-grained Dataset Distillation. FD$^{2}$ localizes discriminative regions and constructs fine-grained representations for distillation. During pretraining, counterfactual attention learning aggregates discriminative representations to update class prototypes. During distillation, a fine-grained characteristic constraint aligns each sample with its class prototype while repelling others, and a similarity constraint diversifies attention across same-class samples. Experiments on multiple fine-grained and general datasets show that FD$^{2}$ integrates seamlessly with decoupled DD and improves performance in most settings, indicating strong transferability.