EVLF: Early Vision-Language Fusion for Generative Dataset Distillation
This work provides an incremental improvement for researchers and practitioners working on generative dataset distillation by producing more visually coherent and semantically faithful synthetic data.
This paper addresses the issue in diffusion-based dataset distillation where late-stage semantic guidance leads to over-corrected samples. The authors propose an Early Vision-Language Fusion (EVLF) method that aligns textual and visual embeddings earlier, resulting in synthetic data that improves downstream classification accuracy across various settings.
Dataset distillation (DD) aims to synthesize compact training sets that enable models to achieve high accuracy with significantly fewer samples. Recent diffusion-based DD methods commonly introduce semantic guidance through late-stage cross-attention, where textual prompts tend to dominate the generative process. Although this strategy enforces label relevance, it diminishes the contribution of visual latents, resulting in over-corrected samples that mirror prompt patterns rather than reflecting intrinsic visual features. To solve this problem, we introduce an Early Vision-Language Fusion (EVLF) method that aligns textual and visual embeddings at the transition between the encoder and the generative backbone. By incorporating a lightweight cross-attention module at this transition, the early representations simultaneously encode local textures and global semantic directions across the denoising process. Importantly, EVLF is plug-and-play and can be easily integrated into any diffusion-based dataset distillation pipeline with an encoder. It works across different denoiser architectures and sampling schedules without any task-specific modifications. Extensive experiments demonstrate that EVLF generates semantically faithful and visually coherent synthetic data, yielding consistent improvements in downstream classification accuracy across varied settings. Source code is available at https://github.com/wenqi-cai297/earlyfusion-for-dd/.