In this work, we present a method to control a text-to-image generative model to produce training data specifically "useful" for supervised learning. Unlike previous works that employ an open-loop approach and pre-define prompts to generate new data using either a language model or human expertise, we develop an automated closed-loop system which involves two feedback mechanisms. The first mechanism uses feedback from a given supervised model and finds adversarial prompts that result in image generations that maximize the model loss. While these adversarial prompts result in diverse data informed by the model, they are not informed of the target distribution, which can be inefficient. Therefore, we introduce the second feedback mechanism that guides the generation process towards a certain target distribution. We call the method combining these two mechanisms Guided Adversarial Prompts. We perform our evaluations on different tasks, datasets and architectures, with different types of distribution shifts (spuriously correlated data, unseen domains) and demonstrate the efficiency of the proposed feedback mechanisms compared to open-loop approaches.

翻译：本文提出了一种方法，用于控制文本到图像生成模型生成对监督学习“有用”的训练数据。与以往采用开环方法、通过语言模型或人类专家预定义提示生成新数据的工作不同，我们开发了一种包含两种反馈机制的自动化闭环系统。第一种机制利用给定监督模型的反馈，寻找能够最大化模型损失的对抗性提示，从而生成图像。尽管这些对抗性提示生成的多样化数据受模型影响，但缺乏对目标分布的感知，可能导致效率低下。为此，我们引入第二种反馈机制，引导生成过程朝向特定目标分布。我们将结合这两种机制的方法称为**引导式对抗提示**。我们针对不同任务、数据集和架构，在多种分布偏移情境（如虚假关联数据、未知域）下进行了评估，并证明了所提出的反馈机制相较于开环方法的有效性。