Reinforcement-Guided Synthetic Data Generation for Privacy-Sensitive Identity Recognition
2026-04-09 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors address the problem of training generative models when there is very little privacy-sensitive data available. They create a method that starts by adapting a general pre-trained generator to the specific target domain. Then, they guide the generator using multiple goals to produce synthetic data that is realistic, diverse, and rich in expressions useful for recognition tasks. Their approach also includes selecting the best synthetic samples during training to improve results. Tests show their method produces better data and improves classification performance, even for new categories with limited data.
generative modelssynthetic datareinforcement learningdomain adaptationprivacy-sensitive datamulti-objective optimizationsemantic consistencydata scarcityclassification accuracy
Authors
Xuemei Jia, Jiawei Du, Hui Wei, Jun Chen, Joey Tianyi Zhou, Zheng Wang
Abstract
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.