Generative Active Learning via Bayesian Acquisition for Improving the Efficiency of Synthetic Data

Unlike real-world datasets, utilizing synthetic data from generative models as train dataset necessitates a rigorous consideration of the training data's informativeness. In this paper, we propose a novel framework designed to steer diffusion models based on Bayesian epistemic uncertainty to train the target model. To ensure computational tractability, we estimate the Bayesian Active Learning by Disagreement (BALD) score by applying the Laplace approximation specifically to the last-layer parameters of the downstream model. Furthermore, we incorporate Feynman-Kac Steering (FKS) for diffusion to enhance the structural diversity of the synthetic samples and mitigate the emergence of undesirable artifacts. Extensive experiments on Imagenette and ImageNet-100 for the classification task and MS-COCO for the object detection task demonstrate that our method significantly outperforms existing baselines in downstream accuracy and OOD generalization. Furthermore, our analysis of pairwise distances between inter-class and intra-class confirms that our method effectively expands the downstream model's knowledge manifold by generating diverse and informative training signals.