Controlled Training Data Generation with Diffusion Models

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.

Unraveling the Key Components of OOD Generalization via Diversification

Real-world datasets may contain multiple features that explain the training data equally well, i.e., learning any of them would lead to correct predictions on the training data. However, many of them can be spurious, i.e., lose their predictive power under a distribution shift and fail to generalize to out-of-distribution (OOD) data. Recently developed ``diversification'' methods approach this problem by finding multiple diverse hypotheses that rely on different features. This paper aims to study this class of methods and identify the key components contributing to their OOD generalization abilities. We show that (1) diversification methods are highly sensitive to the distribution of the unlabeled data used for diversification and can underperform significantly when away from a method-specific sweet spot. (2) Diversification alone is insufficient for OOD generalization. The choice of the used learning algorithm, e.g., the model's architecture and pretraining, is crucial, and using the second-best choice leads to an up to 20% absolute drop in accuracy.(3) The optimal choice of learning algorithm depends on the unlabeled data, and vice versa.Finally, we show that the above pitfalls cannot be alleviated by increasing the number of diverse hypotheses, allegedly the major feature of diversification methods. These findings provide a clearer understanding of the critical design factors influencing the OOD generalization of diversification methods. They can guide practitioners in how to use the existing methods best and guide researchers in developing new, better ones.