We propose a method to generate tailored synthetic training data, i.e., specifically useful for a given supervised model and target deployment domain. We introduce two feedback mechanisms to guide the generation: 1) model-based and 2) target domain-based.

We distill the critical design factors of current state-of-the-art methods (multi-hypotheses/diversification methods) for spurious correlation situations.