面向隐私敏感身份识别的强化引导合成数据生成
Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition
把通用域生成先验通过冷启动适配+多目标奖励（语义一致性/覆盖多样性/表情丰富度）迁移到隐私受限的身份识别域，并用动态样本筛选提升下游分类；这套「奖励引导生成+效用筛选」流程可直接复用到任何标注稀缺的视觉识别任务中做数据增广。
原文依据 we propose a reinforcement-guided synthetic data generation framework that adapts general-domain generative priors to privacy-sensitive identity recognition tasks
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在隐私敏感场景中，由于监管与版权限制，数据获取受到严重制约，因此对高保真生成模型的需求日益增长。这种数据稀缺阻碍了模型开发——颇具讽刺意味的是，恰恰是在最需要生成模型来弥补数据不足的场景中出现了这一问题。这形成了一个自我强化的困境：有限的数据导致生成模型质量不佳，而质量不佳的生成模型又无法缓解数据稀缺。为打破这一循环，我们提出了一种强化引导的合成数据生成框架，将通用域的生成先验适配到隐私敏感的身份识别任务上。我们首先进行冷启动适配，使预训练生成器与目标域对齐，从而建立语义相关性与初始保真度。在此基础上，我们引入一个多目标奖励，联合优化语义一致性、覆盖多样性与表情丰富度，引导生成器产生既真实又对任务有效的样本。在下游训练过程中，动态样本选择机制进一步优先选取高价值的合成样本，从而实现自适应数据扩展并改善域对齐。在基准数据集上的大量实验表明，我们的框架显著提升了生成保真度与分类准确率，同时在小数据条件下对新类别也展现出很强的泛化能力。
英文原文摘要 1439 字符
High-fidelity generative models are increasingly needed in privacy-sensitive scenarios, where access to data is severely restricted due to regulatory and copyright constraints. This scarcity hampers model development--ironically, in settings where generative models are most needed to compensate for the lack of data. This creates a self-reinforcing challenge: limited data leads to poor generative models, which in turn fail to mitigate data scarcity. To break this cycle, we propose a reinforcement-guided synthetic data generation framework that adapts general-domain generative priors to privacy-sensitive identity recognition tasks. We first perform a cold-start adaptation to align a pretrained generator with the target domain, establishing semantic relevance and initial fidelity. Building on this foundation, we introduce a multi-objective reward that jointly optimizes semantic consistency, coverage diversity, and expression richness, guiding the generator to produce both realistic and task-effective samples. During downstream training, a dynamic sample selection mechanism further prioritizes high-utility synthetic samples, enabling adaptive data scaling and improved domain alignment. Extensive experiments on benchmark datasets demonstrate that our framework significantly improves both generation fidelity and classification accuracy, while also exhibiting strong generalization to novel categories in small-data regimes.