Abstract
In this paper, we propose ScoreMix, a novel yet simple data augmentation strategy leveraging the score compositional properties of diffusion models to enhance discriminator performance, particularly under scenarios with limited labeled data. By convexly mixing the scores from different class-conditioned trajectories during diffusion sampling, we generate challenging synthetic samples that significantly improve discriminative capabilities in all studied benchmarks. We systematically investigate class-selection strategies for mixing and discover that greater performance gains arise when combining classes distant in the discriminator's embedding space, rather than close in the generator's condition space. Moreover, we empirically show that, under standard metrics, the correlation between the generator's learned condition space and the discriminator's embedding space is minimal. Our approach achieves notable performance improvements without extensive parameter searches, demonstrating practical advantages for training discriminative models while effectively mitigating problems regarding collections of large datasets. Paper website: this https URL
Abstract (translated)
在这篇论文中，我们提出了ScoreMix，这是一种新颖且简单的数据增强策略，利用扩散模型的分数组合特性来提升判别器在标注数据有限场景下的性能。通过将不同类条件轨迹在扩散采样过程中得到的分数进行凸混合处理，我们生成了具有挑战性的合成样本，这些样本显著提升了所有研究基准中的判别能力。系统地调查了用于混合的类别选择策略，并发现当结合在判别器嵌入空间中距离较远而非生成器条件空间中接近的类时，可以获得更大的性能提升。此外，实证研究表明，在标准度量下，生成器学习到的条件空间与判别器的嵌入空间之间的相关性极小。我们的方法无需广泛的参数搜索即可实现显著的性能改进，展示了在训练判别模型方面的实用优势，并有效地缓解了关于大规模数据集收集的问题。 论文网站: [这里插入链接]
URL
https://arxiv.org/abs/2506.10226
