Controlled Training Data Generation with Diffusion Models
This work addresses the challenge of efficiently producing targeted training data for machine learning models, particularly in handling distribution shifts, but it is incremental as it builds on existing generative and adversarial techniques.
The authors tackled the problem of generating controlled training data for supervised learning using text-to-image diffusion models, by developing an automated closed-loop system with two feedback mechanisms that outperformed open-loop approaches across various tasks, datasets, and distribution shifts.
We present a method to control a text-to-image generative model to produce training data 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.